Open In Colab

In [ ]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

Objectif de la mission

Dans l'optique d'un développement à l'international, notre entreprise de cours en ligne souhaite avoir plus d'information sur le marché mondial. Elle souhaite définir ses priorités d'implantation à partir de différents critères.

Pour cela, il me faut réaliser une analyser exploraroire des données sur les mondiales de l'éducation (Taux d'investissement dans l'éducation, nombre d'élèves, taux d'équipement en internet...)

Besoin exprimé

Notre entreprise souhaite avoir des réponses à ces questions :

  • Quels sont les pays avec un fort potentiel de clients pour nos services ?
  • Pour chacun de ces pays, quelle sera l’évolution de ce potentiel de clients ?
  • Dans quels pays notre entreprise doit-elle opérer en priorité ?

Mode opératoire:

  • Valider la qualité de ce jeu de données (comporte-t-il beaucoup de données manquantes, dupliquées ?)
  • Décrire les informations contenues dans le jeu de données (nombre de colonnes ? nombre de lignes ?)
  • Sélectionner les informations qui semblent pertinentes pour répondre à la problématique (quelles sont les colonnes contenant des informations qui peuvent être utiles pour répondre à la problématique de l’entreprise ?)
  • Déterminer des ordres de grandeurs des indicateurs statistiques classiques pour les différentes zones géographiques et pays du monde (moyenne/médiane/écart-type par pays et par continent ou bloc géographique)

Avant-Propos

Pour notre étude, nous nous appuierons sur différentes données : EdStatsCountry, EdStatsCountry-Series, EdStatsData, EdStatsFootnote et EdStatsSeries.

Avant de détailler, il est important de préciser que seul les éléments retenus dans l'étude seront évoqués dans la description des données.
Présentons maintenant nos données:

  • EdStatsCountry comporte des données sur la région et le revenu des pays.
  • EdStatsCountry-Series contient des données qui auraient pu nous servir en tant que clé, mais nous ne les avons pas retenus
  • EdStatsFootNote comportait des séries comme le 'Country Code' qui auraient pu nous servir de clés mais nous ne l'avons pas retenus
  • EdStatsData comporte le nom des pays, les indicateurs qui y sont rattachés ainsi que les données allant de l'année 1970 à des données prévisionnelles au delà de 2021. Ce dataframe nous a permis de définir différentes clés pour notre traitement de données. Nous nous sommes ainsi servi de ces series : 'Indicator Name', 'Country Name' et 'Region'.
  • EdStatsSeries nous a permis de lire les descriptions complètes des différentes indicateurs afin de mieux les sélectionner.

Chargement des fichiers nécessaires

Exemple où on récupère un fichier depuis le web , en local

In [ ]:
!wget https://s3-eu-west-1.amazonaws.com/static.oc-static.com/prod/courses/files/parcours-data-analyst/DAN-P4-FAO.zip
!unzip DAN-P4-FAO.zip

Un exemple pour charger un dataframe directement depuis le web

In [ ]:
erp_df = pd.read_excel('https://s3-eu-west-1.amazonaws.com/static.oc-static.com/prod/courses/files/parcours-data-analyst/P5+ERP+vin/erp.xlsx')
/usr/local/lib/python3.7/dist-packages/openpyxl/worksheet/_reader.py:312: UserWarning: Unknown extension is not supported and will be removed
  warn(msg)

Recuperer le fichier en local

In [ ]:
!wget https://s3-eu-west-1.amazonaws.com/static.oc-static.com/prod/courses/files/parcours-data-analyst/P5+ERP+vin/web.xlsx
In [ ]:
df_web = pd.read_excel('web.xlsx')
/usr/local/lib/python3.7/dist-packages/openpyxl/worksheet/_reader.py:312: UserWarning: Unknown extension is not supported and will be removed
  warn(msg)
In [ ]:
df_web.head()
In [ ]:
erp_df.head()
Out[ ]:
product_id onsale_web price stock_quantity stock_status
0 3847 1 24.2 0 outofstock
1 3849 1 34.3 0 outofstock
2 3850 1 20.8 0 outofstock
3 4032 1 14.1 0 outofstock
4 4039 1 46.0 0 outofstock
In [ ]:
# On donne le chemin d'accès du fichier qu'on souhaite afficher. On enregistre la lecture de ce fichier, ici sous le nom "Country".
# Dans les propriétés du fichier à afficher, aller dans l'onglet sécurité puis copier le "nom de l'objet" en entier. Attention : Il ressemble à un chemin d'accès, ne pas confondre.
# Penser à toujours ajouter r juste après la parenthèse, vérifier également les parenthèses
# Pour afficher quelques lignes du dataframe, on utilise la commande data.head() 

Country = pd.read_csv('EdStatsCountry.csv')
Country.head()
Out[ ]:
Country Code Short Name Table Name Long Name 2-alpha code Currency Unit Special Notes Region Income Group WB-2 code ... IMF data dissemination standard Latest population census Latest household survey Source of most recent Income and expenditure data Vital registration complete Latest agricultural census Latest industrial data Latest trade data Latest water withdrawal data Unnamed: 31
0 ABW Aruba Aruba Aruba AW Aruban florin SNA data for 2000-2011 are updated from offici... Latin America & Caribbean High income: nonOECD AW ... NaN 2010 NaN NaN Yes NaN NaN 2012.0 NaN NaN
1 AFG Afghanistan Afghanistan Islamic State of Afghanistan AF Afghan afghani Fiscal year end: March 20; reporting period fo... South Asia Low income AF ... General Data Dissemination System (GDDS) 1979 Multiple Indicator Cluster Survey (MICS), 2010/11 Integrated household survey (IHS), 2008 NaN 2013/14 NaN 2012.0 2000 NaN
2 AGO Angola Angola People's Republic of Angola AO Angolan kwanza April 2013 database update: Based on IMF data,... Sub-Saharan Africa Upper middle income AO ... General Data Dissemination System (GDDS) 1970 Malaria Indicator Survey (MIS), 2011 Integrated household survey (IHS), 2008 NaN 2015 NaN NaN 2005 NaN
3 ALB Albania Albania Republic of Albania AL Albanian lek NaN Europe & Central Asia Upper middle income AL ... General Data Dissemination System (GDDS) 2011 Demographic and Health Survey (DHS), 2008/09 Living Standards Measurement Study Survey (LSM... Yes 2012 2010.0 2012.0 2006 NaN
4 AND Andorra Andorra Principality of Andorra AD Euro NaN Europe & Central Asia High income: nonOECD AD ... NaN 2011. Population figures compiled from adminis... NaN NaN Yes NaN NaN 2006.0 NaN NaN

5 rows × 32 columns

In [ ]:
Country_Series = pd.read_csv(r'C:\Users\Clément\Documents\Data Openclassrooms\Fichiers projets 1\Fichier Edstat .csv\data\EdStatsCountry-Series.csv')
Country_Series.head()
---------------------------------------------------------------------------
FileNotFoundError                         Traceback (most recent call last)
<ipython-input-9-3467b34bdf8a> in <module>()
----> 1 Country_Series = pd.read_csv(r'C:\Users\Clément\Documents\Data Openclassrooms\Fichiers projets 1\Fichier Edstat .csv\data\EdStatsCountry-Series.csv')
      2 Country_Series.head()

/usr/local/lib/python3.7/dist-packages/pandas/util/_decorators.py in wrapper(*args, **kwargs)
    309                     stacklevel=stacklevel,
    310                 )
--> 311             return func(*args, **kwargs)
    312 
    313         return wrapper

/usr/local/lib/python3.7/dist-packages/pandas/io/parsers/readers.py in read_csv(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, error_bad_lines, warn_bad_lines, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)
    584     kwds.update(kwds_defaults)
    585 
--> 586     return _read(filepath_or_buffer, kwds)
    587 
    588 

/usr/local/lib/python3.7/dist-packages/pandas/io/parsers/readers.py in _read(filepath_or_buffer, kwds)
    480 
    481     # Create the parser.
--> 482     parser = TextFileReader(filepath_or_buffer, **kwds)
    483 
    484     if chunksize or iterator:

/usr/local/lib/python3.7/dist-packages/pandas/io/parsers/readers.py in __init__(self, f, engine, **kwds)
    809             self.options["has_index_names"] = kwds["has_index_names"]
    810 
--> 811         self._engine = self._make_engine(self.engine)
    812 
    813     def close(self):

/usr/local/lib/python3.7/dist-packages/pandas/io/parsers/readers.py in _make_engine(self, engine)
   1038             )
   1039         # error: Too many arguments for "ParserBase"
-> 1040         return mapping[engine](self.f, **self.options)  # type: ignore[call-arg]
   1041 
   1042     def _failover_to_python(self):

/usr/local/lib/python3.7/dist-packages/pandas/io/parsers/c_parser_wrapper.py in __init__(self, src, **kwds)
     49 
     50         # open handles
---> 51         self._open_handles(src, kwds)
     52         assert self.handles is not None
     53 

/usr/local/lib/python3.7/dist-packages/pandas/io/parsers/base_parser.py in _open_handles(self, src, kwds)
    227             memory_map=kwds.get("memory_map", False),
    228             storage_options=kwds.get("storage_options", None),
--> 229             errors=kwds.get("encoding_errors", "strict"),
    230         )
    231 

/usr/local/lib/python3.7/dist-packages/pandas/io/common.py in get_handle(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)
    705                 encoding=ioargs.encoding,
    706                 errors=errors,
--> 707                 newline="",
    708             )
    709         else:

FileNotFoundError: [Errno 2] No such file or directory: 'C:\\Users\\Clément\\Documents\\Data Openclassrooms\\Fichiers projets 1\\Fichier Edstat .csv\\data\\EdStatsCountry-Series.csv'
In [ ]:
Data = pd.read_csv(r'C:\Users\Clément\Documents\Data Openclassrooms\Fichiers projets 1\Fichier Edstat .csv\data\EdStatsData.csv')
Data.head()
Out[ ]:
Country Name Country Code Indicator Name Indicator Code 1970 1971 1972 1973 1974 1975 ... 2060 2065 2070 2075 2080 2085 2090 2095 2100 Unnamed: 69
0 Arab World ARB Adjusted net enrolment rate, lower secondary, ... UIS.NERA.2 NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
1 Arab World ARB Adjusted net enrolment rate, lower secondary, ... UIS.NERA.2.F NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
2 Arab World ARB Adjusted net enrolment rate, lower secondary, ... UIS.NERA.2.GPI NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
3 Arab World ARB Adjusted net enrolment rate, lower secondary, ... UIS.NERA.2.M NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
4 Arab World ARB Adjusted net enrolment rate, primary, both sex... SE.PRM.TENR 54.822121 54.894138 56.209438 57.267109 57.991138 59.36554 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN

5 rows × 70 columns

In [ ]:
FootNote = pd.read_csv(r'C:\Users\Clément\Documents\Data Openclassrooms\Fichiers projets 1\Fichier Edstat .csv\data\EdStatsFootNote.csv')
FootNote.head()
Out[ ]:
CountryCode SeriesCode Year DESCRIPTION Unnamed: 4
0 ABW SE.PRE.ENRL.FE YR2001 Country estimation. NaN
1 ABW SE.TER.TCHR.FE YR2005 Country estimation. NaN
2 ABW SE.PRE.TCHR.FE YR2000 Country estimation. NaN
3 ABW SE.SEC.ENRL.GC YR2004 Country estimation. NaN
4 ABW SE.PRE.TCHR YR2006 Country estimation. NaN
In [ ]:
Series = pd.read_csv(r'C:\Users\Clément\Documents\Data Openclassrooms\Fichiers projets 1\Fichier Edstat .csv\data\EdStatsSeries.csv')
Series.head()
Out[ ]:
Series Code Topic Indicator Name Short definition Long definition Unit of measure Periodicity Base Period Other notes Aggregation method ... Notes from original source General comments Source Statistical concept and methodology Development relevance Related source links Other web links Related indicators License Type Unnamed: 20
0 BAR.NOED.1519.FE.ZS Attainment Barro-Lee: Percentage of female population age... Percentage of female population age 15-19 with... Percentage of female population age 15-19 with... NaN NaN NaN NaN NaN ... NaN NaN Robert J. Barro and Jong-Wha Lee: http://www.b... NaN NaN NaN NaN NaN NaN NaN
1 BAR.NOED.1519.ZS Attainment Barro-Lee: Percentage of population age 15-19 ... Percentage of population age 15-19 with no edu... Percentage of population age 15-19 with no edu... NaN NaN NaN NaN NaN ... NaN NaN Robert J. Barro and Jong-Wha Lee: http://www.b... NaN NaN NaN NaN NaN NaN NaN
2 BAR.NOED.15UP.FE.ZS Attainment Barro-Lee: Percentage of female population age... Percentage of female population age 15+ with n... Percentage of female population age 15+ with n... NaN NaN NaN NaN NaN ... NaN NaN Robert J. Barro and Jong-Wha Lee: http://www.b... NaN NaN NaN NaN NaN NaN NaN
3 BAR.NOED.15UP.ZS Attainment Barro-Lee: Percentage of population age 15+ wi... Percentage of population age 15+ with no educa... Percentage of population age 15+ with no educa... NaN NaN NaN NaN NaN ... NaN NaN Robert J. Barro and Jong-Wha Lee: http://www.b... NaN NaN NaN NaN NaN NaN NaN
4 BAR.NOED.2024.FE.ZS Attainment Barro-Lee: Percentage of female population age... Percentage of female population age 20-24 with... Percentage of female population age 20-24 with... NaN NaN NaN NaN NaN ... NaN NaN Robert J. Barro and Jong-Wha Lee: http://www.b... NaN NaN NaN NaN NaN NaN NaN

5 rows × 21 columns

In [ ]:
# On veut afficher les intitulés des colonnes
# L'objectif est de fusionner les dataframes qui ont au moins un intitulé en commun, dans le cas contraire, il y aura une erreur.
# On donne le nom du dataframe concerné puis on ajoute "".columns" ---->  nomdudataframe.columns

Country.columns
Out[ ]:
Index(['Country Code', 'Short Name', 'Table Name', 'Long Name', '2-alpha code',
       'Currency Unit', 'Special Notes', 'Region', 'Income Group', 'WB-2 code',
       'National accounts base year', 'National accounts reference year',
       'SNA price valuation', 'Lending category', 'Other groups',
       'System of National Accounts', 'Alternative conversion factor',
       'PPP survey year', 'Balance of Payments Manual in use',
       'External debt Reporting status', 'System of trade',
       'Government Accounting concept', 'IMF data dissemination standard',
       'Latest population census', 'Latest household survey',
       'Source of most recent Income and expenditure data',
       'Vital registration complete', 'Latest agricultural census',
       'Latest industrial data', 'Latest trade data',
       'Latest water withdrawal data', 'Unnamed: 31'],
      dtype='object')
In [ ]:
Country_Series.columns
Out[ ]:
Index(['CountryCode', 'SeriesCode', 'DESCRIPTION', 'Unnamed: 3'], dtype='object')
In [ ]:
FootNote.columns
Out[ ]:
Index(['CountryCode', 'SeriesCode', 'Year', 'DESCRIPTION', 'Unnamed: 4'], dtype='object')
In [ ]:
Series.columns
Out[ ]:
Index(['Series Code', 'Topic', 'Indicator Name', 'Short definition',
       'Long definition', 'Unit of measure', 'Periodicity', 'Base Period',
       'Other notes', 'Aggregation method', 'Limitations and exceptions',
       'Notes from original source', 'General comments', 'Source',
       'Statistical concept and methodology', 'Development relevance',
       'Related source links', 'Other web links', 'Related indicators',
       'License Type', 'Unnamed: 20'],
      dtype='object')

Fusion des dataframes à partir de variables communes

In [ ]:
# Je veux fusionner l'ensemble des données

Data_Country = pd.merge(Data, Country, on='Country Code', how= 'left')
Data_Country.head()
Out[ ]:
Country Name Country Code Indicator Name Indicator Code 1970 1971 1972 1973 1974 1975 ... IMF data dissemination standard Latest population census Latest household survey Source of most recent Income and expenditure data Vital registration complete Latest agricultural census Latest industrial data Latest trade data Latest water withdrawal data Unnamed: 31
0 Arab World ARB Adjusted net enrolment rate, lower secondary, ... UIS.NERA.2 NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
1 Arab World ARB Adjusted net enrolment rate, lower secondary, ... UIS.NERA.2.F NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
2 Arab World ARB Adjusted net enrolment rate, lower secondary, ... UIS.NERA.2.GPI NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
3 Arab World ARB Adjusted net enrolment rate, lower secondary, ... UIS.NERA.2.M NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
4 Arab World ARB Adjusted net enrolment rate, primary, both sex... SE.PRM.TENR 54.822121 54.894138 56.209438 57.267109 57.991138 59.36554 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN

5 rows × 101 columns

In [ ]:
#Je souhaite vérifier que le merge s'est correctement passé en comparant les anciennes et les nouvelles proportions des dataframes

print(Country.shape)
print(Data.shape)
print(Data_Country.shape)
(241, 32)
(886930, 70)
(886930, 101)
In [ ]:
Data_Country.columns
Out[ ]:
Index(['Country Name', 'Country Code', 'Indicator Name', 'Indicator Code',
       '1970', '1971', '1972', '1973', '1974', '1975',
       ...
       'IMF data dissemination standard', 'Latest population census',
       'Latest household survey',
       'Source of most recent Income and expenditure data',
       'Vital registration complete', 'Latest agricultural census',
       'Latest industrial data', 'Latest trade data',
       'Latest water withdrawal data', 'Unnamed: 31'],
      dtype='object', length=101)
In [ ]:
Data_Country_cols = Data_Country.columns
In [ ]:
# La fonction ".sum" indique que je veux la somme de  l'ensemble des données nulles par colonne.
# Aucune valeur nulle n'est présente dans la colonne "Country Name"
# 814 642 valeurs sont manquantes dans la colonne correspondante à l'année 1970.
# IMPORTANT : Dans pandas, .sum va convertir les valeurs True et False en valeur numérique. True = 1 et False = 0

Data_Country.isnull().sum()
Out[ ]:
Country Name                         0
Country Code                         0
Indicator Name                       0
Indicator Code                       0
1970                            814642
                                 ...  
Latest agricultural census      366500
Latest industrial data          494775
Latest trade data               208905
Latest water withdrawal data    230895
Unnamed: 31                     886930
Length: 101, dtype: int64
In [ ]:
# Rappel :

# axis=0 lis les données, de haut en bas dans le dataframe. Dans le sens des lignes donc.
# axis=1 ---------------- de haut en bas -----------------. Dans le sens des colonnes.    

# La fonction drop.na fonction par défaut avec axis=0

# La fonction drop.na sert à se débarasser des valeurs manquantes.
# Ici, en précisant (how='all'), je demande à me débarasser de la totalité des lignes (de la variable) 
# si l'ensemble, ("all") des valeurs y sont manquantes.
# How='any' : si au moins une valeur est manquantes, on se débarrasse de la variable.

Data_Country.dropna(how='all').shape
Out[ ]:
(886930, 101)
In [ ]:
# La fonction précédente n'a pas été efficace. On va affecter un taux de valeurs manquantes à la fonction pour savoir si on garde la variables concernées.

len(Data_Country)
Out[ ]:
886930
In [ ]:
df1 = pd.merge(Country, Data, how= 'left')
df1.head()
Out[ ]:
Country Code Short Name Table Name Long Name 2-alpha code Currency Unit Special Notes Region Income Group WB-2 code ... 2060 2065 2070 2075 2080 2085 2090 2095 2100 Unnamed: 69
0 ABW Aruba Aruba Aruba AW Aruban florin SNA data for 2000-2011 are updated from offici... Latin America & Caribbean High income: nonOECD AW ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
1 ABW Aruba Aruba Aruba AW Aruban florin SNA data for 2000-2011 are updated from offici... Latin America & Caribbean High income: nonOECD AW ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
2 ABW Aruba Aruba Aruba AW Aruban florin SNA data for 2000-2011 are updated from offici... Latin America & Caribbean High income: nonOECD AW ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
3 ABW Aruba Aruba Aruba AW Aruban florin SNA data for 2000-2011 are updated from offici... Latin America & Caribbean High income: nonOECD AW ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
4 ABW Aruba Aruba Aruba AW Aruban florin SNA data for 2000-2011 are updated from offici... Latin America & Caribbean High income: nonOECD AW ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN

5 rows × 101 columns

In [ ]:
df1.shape
Out[ ]:
(886930, 101)
In [ ]:
df2 = pd.merge(Series, df1, how= 'left')
df2.head()
Out[ ]:
Series Code Topic Indicator Name Short definition Long definition Unit of measure Periodicity Base Period Other notes Aggregation method ... 2060 2065 2070 2075 2080 2085 2090 2095 2100 Unnamed: 69
0 BAR.NOED.1519.FE.ZS Attainment Barro-Lee: Percentage of female population age... Percentage of female population age 15-19 with... Percentage of female population age 15-19 with... NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
1 BAR.NOED.1519.FE.ZS Attainment Barro-Lee: Percentage of female population age... Percentage of female population age 15-19 with... Percentage of female population age 15-19 with... NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
2 BAR.NOED.1519.FE.ZS Attainment Barro-Lee: Percentage of female population age... Percentage of female population age 15-19 with... Percentage of female population age 15-19 with... NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
3 BAR.NOED.1519.FE.ZS Attainment Barro-Lee: Percentage of female population age... Percentage of female population age 15-19 with... Percentage of female population age 15-19 with... NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
4 BAR.NOED.1519.FE.ZS Attainment Barro-Lee: Percentage of female population age... Percentage of female population age 15-19 with... Percentage of female population age 15-19 with... NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN

5 rows × 121 columns

In [ ]:
df2.shape
Out[ ]:
(887392, 121)
In [ ]:
# Je cible les colonnes que je souhaite conserver parmis celles que j'ai présélectionné.

cols = ['Indicator Name', 'Long definition', 'Unit of measure', 'Periodicity', 'Base Period', 'Development relevance', 'Related indicators', 'Region', 'Income Group', 'SNA price valuation', 'Lending category', 'System of National Accounts', 'Alternative conversion factor', 'PPP survey year', 'External debt Reporting status', 'Latest population census', 'Latest household survey', 'Source of most recent Income and expenditure data', 'Latest agricultural census', 'Latest industrial data', 'Latest trade data', 'Country Name', '2000', '2001', '2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009', '2010', '2011', '2012', '2013', '2014', '2015', '2016', '2017']
data_to_study = df2[cols]
data_to_study.head()
Out[ ]:
Indicator Name Long definition Unit of measure Periodicity Base Period Development relevance Related indicators Region Income Group SNA price valuation ... 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
0 Barro-Lee: Percentage of female population age... Percentage of female population age 15-19 with... NaN NaN NaN NaN NaN Latin America & Caribbean High income: nonOECD Value added at basic prices (VAB) ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
1 Barro-Lee: Percentage of female population age... Percentage of female population age 15-19 with... NaN NaN NaN NaN NaN South Asia Low income Value added at basic prices (VAB) ... NaN NaN 59.18 NaN NaN NaN NaN NaN NaN NaN
2 Barro-Lee: Percentage of female population age... Percentage of female population age 15-19 with... NaN NaN NaN NaN NaN Sub-Saharan Africa Upper middle income Value added at producer prices (VAP) ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
3 Barro-Lee: Percentage of female population age... Percentage of female population age 15-19 with... NaN NaN NaN NaN NaN Europe & Central Asia Upper middle income Value added at basic prices (VAB) ... NaN NaN 0.95 NaN NaN NaN NaN NaN NaN NaN
4 Barro-Lee: Percentage of female population age... Percentage of female population age 15-19 with... NaN NaN NaN NaN NaN Europe & Central Asia High income: nonOECD NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN

5 rows × 40 columns

In [ ]:
data_to_study.shape
Out[ ]:
(887392, 40)

Filtrage des valeurs manquantes

In [ ]:
# On va affecter un taux de valeurs manquantes à la fonction pour savoir si on garde la variables concernées.

df4 = data_to_study.dropna(thresh=len( data_to_study.columns)*0.7)
In [ ]:
 
In [ ]:
df4
Out[ ]:
Indicator Name Long definition Unit of measure Periodicity Base Period Development relevance Related indicators Region Income Group SNA price valuation ... 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
147863 Internet users (per 100 people) Internet users are individuals who have used t... NaN Annual NaN NaN NaN South Asia Low income Value added at basic prices (VAB) ... 1.840000 3.55 4.000000 5.000000 5.454545 5.9 7.0 8.260000 10.595726 NaN
147864 Internet users (per 100 people) Internet users are individuals who have used t... NaN Annual NaN NaN NaN Sub-Saharan Africa Upper middle income Value added at producer prices (VAP) ... 1.900000 2.30 2.800000 3.100000 6.500000 8.9 10.2 12.400000 13.000000 NaN
147865 Internet users (per 100 people) Internet users are individuals who have used t... NaN Annual NaN NaN NaN Europe & Central Asia Upper middle income Value added at basic prices (VAB) ... 23.860000 41.20 45.000000 49.000000 54.655959 57.2 60.1 63.252933 66.363445 NaN
147868 Internet users (per 100 people) Internet users are individuals who have used t... NaN Annual NaN NaN NaN Middle East & North Africa High income: nonOECD Value added at producer prices (VAP) ... 63.000000 64.00 68.000000 78.000000 84.999991 88.0 90.4 90.500000 90.600007 NaN
147869 Internet users (per 100 people) Internet users are individuals who have used t... NaN Annual NaN NaN NaN Latin America & Caribbean Upper middle income Value added at basic prices (VAB) ... 28.112623 34.00 45.000000 51.000000 55.800000 59.9 64.7 68.043064 70.150764 NaN
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
812849 Unemployment, total (% of total labor force) NaN NaN NaN NaN NaN NaN East Asia & Pacific Lower middle income Value added at basic prices (VAB) ... 2.300000 4.40 8.000000 5.700000 8.700000 8.8 8.7 8.800000 7.700000 NaN
812851 Unemployment, total (% of total labor force) NaN NaN NaN NaN NaN NaN Middle East & North Africa Lower middle income Value added at producer prices (VAP) ... 15.000000 14.60 17.799999 17.299999 17.600000 17.6 17.4 16.700001 16.200001 NaN
812852 Unemployment, total (% of total labor force) NaN NaN NaN NaN NaN NaN Sub-Saharan Africa Upper middle income Value added at basic prices (VAB) ... 22.400000 23.50 24.700001 24.600000 24.700001 24.6 24.9 25.200001 26.700001 NaN
812853 Unemployment, total (% of total labor force) NaN NaN NaN NaN NaN NaN Sub-Saharan Africa Lower middle income Value added at basic prices (VAB) ... 7.900000 8.60 10.800000 13.600000 7.800000 7.8 7.7 7.700000 7.600000 NaN
812854 Unemployment, total (% of total labor force) NaN NaN NaN NaN NaN NaN Sub-Saharan Africa Low income Value added at basic prices (VAB) ... 5.400000 5.50 6.200000 5.400000 5.300000 5.2 5.1 5.100000 5.200000 NaN

71758 rows × 40 columns

In [ ]:
# J'affiche les lignes uniques de mon df'

df4['Indicator Name'].unique
Out[ ]:
<bound method Series.unique of 147863                 Internet users (per 100 people)
147864                 Internet users (per 100 people)
147865                 Internet users (per 100 people)
147868                 Internet users (per 100 people)
147869                 Internet users (per 100 people)
                              ...                     
812849    Unemployment, total (% of total labor force)
812851    Unemployment, total (% of total labor force)
812852    Unemployment, total (% of total labor force)
812853    Unemployment, total (% of total labor force)
812854    Unemployment, total (% of total labor force)
Name: Indicator Name, Length: 71758, dtype: object>
In [ ]:
# Je convertis ces lignes en liste

df4['Indicator Name'].unique().tolist()
Out[ ]:
['Internet users (per 100 people)',
 'GDP, PPP (current international $)',
 'GDP, PPP (constant 2011 international $)',
 'GDP per capita (current US$)',
 'GDP per capita, PPP (current international $)',
 'GDP per capita, PPP (constant 2011 international $)',
 'GNI (current US$)',
 'GNI, PPP (current international $)',
 'GNI per capita, Atlas method (current US$)',
 'GNI per capita, PPP (current international $)',
 'Annual statutory teacher salaries in public institutions in USD. Primary. Starting salary',
 'Annual statutory teacher salaries in public institutions in USD. Primary. 15 years of experience',
 'Annual statutory teacher salaries in public institutions in USD. Primary. Top of scale',
 'Annual statutory teacher salaries in public institutions in USD. Lower Secondary. Starting salary',
 'Annual statutory teacher salaries in public institutions in USD. Lower Secondary. 15 years of experience',
 'Annual statutory teacher salaries in public institutions in USD. Lower Secondary. Top of scale',
 'Annual statutory teacher salaries in public institutions in USD. Upper Secondary. Starting salary',
 'Annual statutory teacher salaries in public institutions in USD. Upper Secondary. 15 years of experience',
 'Annual statutory teacher salaries in public institutions in USD. Upper Secondary. Top of scale',
 'Youth literacy rate, population 15-24 years, female (%)',
 'Youth literacy rate, population 15-24 years, gender parity index (GPI)',
 'Youth literacy rate, population 15-24 years, male (%)',
 'Youth literacy rate, population 15-24 years, both sexes (%)',
 'Adult literacy rate, population 15+ years, female (%)',
 'Adult literacy rate, population 15+ years, male (%)',
 'Adult literacy rate, population 15+ years, both sexes (%)',
 'Duration of compulsory education (years)',
 'Gross enrolment ratio, primary, gender parity index (GPI)',
 'Gross enrolment ratio, primary and secondary, gender parity index (GPI)',
 'Gross enrolment ratio, secondary, gender parity index (GPI)',
 'Gross enrolment ratio, tertiary, gender parity index (GPI)',
 'Enrolment in pre-primary education, both sexes (number)',
 'Enrolment in pre-primary education, female (number)',
 'Percentage of students in pre-primary education who are female (%)',
 'Pupil-teacher ratio in pre-primary education (headcount basis)',
 'Gross enrolment ratio, pre-primary, both sexes (%)',
 'Gross enrolment ratio, pre-primary, female (%)',
 'Gross enrolment ratio, pre-primary, male (%)',
 'Net enrolment rate, pre-primary, both sexes (%)',
 'Net enrolment rate, pre-primary, female (%)',
 'Net enrolment rate, pre-primary, male (%)',
 'Percentage of enrolment in pre-primary education in private institutions (%)',
 'Teachers in pre-primary education, both sexes (number)',
 'Teachers in pre-primary education, female (number)',
 'Percentage of teachers in pre-primary education who are female (%)',
 'Official entrance age to primary education (years)',
 'Gross graduation ratio from primary education, female (%)',
 'Gross graduation ratio from primary education, male (%)',
 'Gross graduation ratio from primary education, both sexes (%)',
 'Primary completion rate, female (%)',
 'Primary completion rate, male (%)',
 'Primary completion rate, both sexes (%)',
 'Cumulative drop-out rate to the last grade of primary education, female (%)',
 'Cumulative drop-out rate to the last grade of primary education, male (%)',
 'Cumulative drop-out rate to the last grade of primary education, both sexes (%)',
 'Theoretical duration of primary education (years)',
 'Enrolment in primary education, both sexes (number)',
 'Enrolment in primary education, female (number)',
 'Percentage of students in primary education who are female (%)',
 'Pupil-teacher ratio in primary education (headcount basis)',
 'Gross enrolment ratio, primary, both sexes (%)',
 'Gross enrolment ratio, primary, female (%)',
 'Gross enrolment ratio, primary, male (%)',
 'Gross intake ratio to Grade 1 of primary education, female (%)',
 'Gross intake ratio to Grade 1 of primary education, male (%)',
 'Gross intake ratio to Grade 1 of primary education, both sexes (%)',
 'Net enrolment rate, primary, both sexes (%)',
 'Net enrolment rate, primary, female (%)',
 'Net enrolment rate, primary, male (%)',
 'Net intake rate to Grade 1 of primary education, female (%)',
 'Net intake rate to Grade 1 of primary education, male (%)',
 'Net intake rate to Grade 1 of primary education, both sexes (%)',
 'Percentage of enrolment in primary education in private institutions (%)',
 'Survival rate to Grade 5 of primary education, female (%)',
 'Survival rate to Grade 5 of primary education, male (%)',
 'Survival rate to Grade 5 of primary education, both sexes (%)',
 'Survival rate to the last grade of primary education, female (%)',
 'Survival rate to the last grade of primary education, male (%)',
 'Survival rate to the last grade of primary education, both sexes (%)',
 'Percentage of repeaters in primary education, all grades, female (%)',
 'Percentage of repeaters in primary education, all grades, male (%)',
 'Percentage of repeaters in primary education, all grades, both sexes (%)',
 'Percentage of female teachers in primary education who are trained, female (%)',
 'Percentage of male teachers in primary education who are trained, male (%)',
 'Percentage of teachers in primary education who are trained, both sexes (%)',
 'Teachers in primary education, both sexes (number)',
 'Teachers in primary education, female (number)',
 'Percentage of teachers in primary education who are female (%)',
 'Adjusted net enrolment rate, primary, both sexes (%)',
 'Adjusted net enrolment rate, primary, female (%)',
 'Adjusted net enrolment rate, primary, male (%)',
 'Out-of-school children of primary school age, both sexes (number)',
 'Out-of-school children of primary school age, female (number)',
 'Out-of-school children of primary school age, male (number)',
 'School life expectancy, primary to tertiary, both sexes (years)',
 'School life expectancy, primary to tertiary, female (years)',
 'School life expectancy, primary to tertiary, male (years)',
 'Official entrance age to lower secondary education (years)',
 'Lower secondary completion rate, female (%)',
 'Lower secondary completion rate, male (%)',
 'Lower secondary completion rate, both sexes (%)',
 'Theoretical duration of secondary education (years)',
 'Theoretical duration of lower secondary education (years)',
 'Theoretical duration of upper secondary education (years)',
 'Enrolment in secondary education, both sexes (number)',
 'Enrolment in secondary education, female (number)',
 'Percentage of female students in secondary education enrolled in vocational programmes, female (%)',
 'Percentage of students in secondary education who are female (%)',
 'Enrolment in secondary general, both sexes (number)',
 'Enrolment in secondary general, female (number)',
 'Percentage of students in secondary general education who are female (%)',
 'Percentage of male students in secondary education enrolled in vocational programmes, male (%)',
 'Pupil-teacher ratio in secondary education (headcount basis)',
 'Enrolment in secondary vocational, both sexes (number)',
 'Enrolment in secondary vocational, female (number)',
 'Percentage of students in secondary vocational education who are female (%)',
 'Percentage of students in secondary education enrolled in vocational programmes, both sexes (%)',
 'Gross enrolment ratio, secondary, both sexes (%)',
 'Gross enrolment ratio, secondary, female (%)',
 'Gross enrolment ratio, lower secondary, both sexes (%)',
 'Gross enrolment ratio, lower secondary, female (%)',
 'Gross enrolment ratio, lower secondary, male (%)',
 'Gross enrolment ratio, secondary, male (%)',
 'Gross enrolment ratio, upper secondary, both sexes (%)',
 'Gross enrolment ratio, upper secondary, female (%)',
 'Gross enrolment ratio, upper secondary, male (%)',
 'Net enrolment rate, secondary, both sexes (%)',
 'Net enrolment rate, secondary, female (%)',
 'Net enrolment rate, secondary, male (%)',
 'Percentage of enrolment in secondary education in private institutions (%)',
 'Effective transition rate from primary to lower secondary general education, female (%)',
 'Effective transition rate from primary to lower secondary general education, male (%)',
 'Effective transition rate from primary to lower secondary general education, both sexes (%)',
 'Percentage of female teachers in secondary education who are trained, female (%)',
 'Percentage of male teachers in secondary education who are trained, male (%)',
 'Percentage of teachers in secondary education who are trained, both sexes (%)',
 'Teachers in secondary education, both sexes (number)',
 'Teachers in secondary education, female (number)',
 'Percentage of teachers in secondary education who are female (%)',
 'Gross graduation ratio from first degree programmes (ISCED 6 and 7) in tertiary education, female (%)',
 'Gross graduation ratio from first degree programmes (ISCED 6 and 7) in tertiary education, male (%)',
 'Gross graduation ratio from first degree programmes (ISCED 6 and 7) in tertiary education, both sexes (%)',
 'Enrolment in tertiary education, all programmes, both sexes (number)',
 'Enrolment in tertiary education, all programmes, female (number)',
 'Percentage of students in tertiary education who are female (%)',
 'Gross enrolment ratio, tertiary, both sexes (%)',
 'Gross enrolment ratio, tertiary, female (%)',
 'Gross enrolment ratio, tertiary, male (%)',
 'Graduates from tertiary education, both sexes (number)',
 'Percentage of female graduates from tertiary education graduating from Agriculture programmes, female (%)',
 'Percentage of graduates from tertiary education graduating from Agriculture programmes, both sexes (%)',
 'Percentage of female graduates from tertiary education graduating from Education programmes, female (%)',
 'Percentage of graduates from tertiary education graduating from Education programmes, both sexes (%)',
 'Percentage of female graduates from tertiary education graduating from Engineering, Manufacturing and Construction programmes, female (%)',
 'Percentage of graduates from tertiary education graduating from Engineering, Manufacturing and Construction programmes, both sexes (%)',
 'Graduates from tertiary education, female (number)',
 'Percentage of graduates from Agriculture programmes in tertiary education who are female (%)',
 'Percentage of graduates from Education programmes in tertiary education who are female (%)',
 'Percentage of graduates from Engineering, Manufacturing and Construction programmes in tertiary education who are female (%)',
 'Percentage of graduates from Health and Welfare programmes in tertiary education who are female (%)',
 'Percentage of graduates from Humanities and Arts programmes in tertiary education who are female (%)',
 'Percentage of graduates from programmes in unspecified fields in tertiary education who are female (%)',
 'Percentage of graduates from Science programmes in tertiary education who are female (%)',
 'Percentage of graduates from Social Sciences, Business and Law programmes in tertiary education who are female (%)',
 'Percentage of graduates from Services programmes in tertiary education who are female (%)',
 'Percentage of graduates from tertiary education who are female (%)',
 'Percentage of female graduates from tertiary education graduating from Health and Welfare programmes, female (%)',
 'Percentage of graduates from tertiary education graduating from Health and Welfare programmes, both sexes (%)',
 'Percentage of female graduates from tertiary education graduating from Humanities and Arts programmes, female (%)',
 'Percentage of graduates from tertiary education graduating from Humanities and Arts programmes, both sexes (%)',
 'Percentage of female graduates from tertiary education graduating from programmes in unspecified fields, female (%)',
 'Percentage of graduates from tertiary education graduating from programmes in unspecified fields, both sexes (%)',
 'Percentage of female graduates from tertiary education graduating from Science programmes, female (%)',
 'Percentage of graduates from tertiary education graduating from Science programmes, both sexes (%)',
 'Percentage of female graduates from tertiary education graduating from Social Sciences, Business and Law programmes, female (%)',
 'Percentage of graduates from tertiary education graduating from Social Sciences, Business and Law programmes, both sexes (%)',
 'Percentage of female graduates from tertiary education graduating from Services programmes, female (%)',
 'Percentage of graduates from tertiary education graduating from Services programmes, both sexes (%)',
 'Percentage of enrolment in tertiary education in private institutions (%)',
 'Teachers in tertiary education programmes, both sexes (number)',
 'Teachers in tertiary education programmes, female (number)',
 'Percentage of teachers in tertiary education who are female (%)',
 'Gross enrolment ratio, primary to tertiary, both sexes (%)',
 'Current expenditure as % of total expenditure in public institutions (%)',
 'Government expenditure per primary student as % of GDP per capita (%)',
 'Expenditure on primary as % of government expenditure on education (%)',
 'Government expenditure per secondary student as % of GDP per capita (%)',
 'Expenditure on secondary as % of government expenditure on education (%)',
 'Government expenditure per tertiary student as % of GDP per capita (%)',
 'Expenditure on tertiary as % of government expenditure on education (%)',
 'Expenditure on education as % of total government expenditure (%)',
 'Government expenditure on education as % of GDP (%)',
 'Prevalence of HIV, total (% of population ages 15-49)',
 'Labor force, female (% of total labor force)',
 'Labor force, total',
 'Population, ages 3-5, female',
 'Population, ages 3-5, male',
 'Population, ages 3-5, total',
 'Population, ages 4-6, female',
 'Population, ages 4-6, male',
 'Population, ages 4-6, total',
 'Population, ages 5-9, female',
 'Population, ages 5-9, male',
 'Population, ages 5-9, total',
 'Population, ages 5-10, female',
 'Population, ages 5-10, male',
 'Population, ages 5-10, total',
 'Population, ages 5-11, female',
 'Population, ages 5-11, male',
 'Population, ages 5-11, total',
 'Population, ages 6-9, female',
 'Population, ages 6-9, male',
 'Population, ages 6-9, total',
 'Population, ages 6-10, female',
 'Population, ages 6-10, male',
 'Population, ages 6-10, total',
 'Population, ages 6-11, female',
 'Population, ages 6-11, male',
 'Population, ages 6-11, total',
 'Population, ages 6-12, female',
 'Population, ages 6-12, male',
 'Population, ages 6-12, total',
 'Population, ages 7-9, female',
 'Population, ages 7-9, male',
 'Population, ages 7-9, total',
 'Population, ages 7-10, female',
 'Population, ages 7-10, male',
 'Population, ages 7-10, total',
 'Population, ages 7-11, female',
 'Population, ages 7-11, male',
 'Population, ages 7-11, total',
 'Population, ages 7-12, female',
 'Population, ages 7-12, male',
 'Population, ages 7-12, total',
 'Population, ages 7-13, female',
 'Population, ages 7-13, male',
 'Population, ages 7-13, total',
 'Population, ages 10-14, female',
 'Population, ages 10-14, male',
 'Population, ages 10-14, total',
 'Population, ages 10-15, female',
 'Population, ages 10-15, male',
 'Population, ages 10-15, total',
 'Population, ages 10-16, female',
 'Population, ages 10-16, male',
 'Population, ages 10-16, total',
 'Population, ages 10-17, female',
 'Population, ages 10-17, male',
 'Population, ages 10-17, total',
 'Population, ages 10-18, female',
 'Population, ages 10-18, male',
 'Population, ages 10-18, total',
 'Population, ages 11-15, female',
 'Population, ages 11-15, male',
 'Population, ages 11-15, total',
 'Population, ages 11-16, female',
 'Population, ages 11-16, male',
 'Population, ages 11-16, total',
 'Population, ages 11-17, female',
 'Population, ages 11-17, male',
 'Population, ages 11-17, total',
 'Population, ages 11-18, female',
 'Population, ages 11-18, male',
 'Population, ages 11-18, total',
 'Population, ages 12-15, female',
 'Population, ages 12-15, male',
 'Population, ages 12-15, total',
 'Population, ages 12-16, female',
 'Population, ages 12-16, male',
 'Population, ages 12-16, total',
 'Population, ages 12-17, female',
 'Population, ages 12-17, male',
 'Population, ages 12-17, total',
 'Population, ages 12-18, female',
 'Population, ages 12-18, male',
 'Population, ages 12-18, total',
 'Population, ages 13-16, female',
 'Population, ages 13-16, male',
 'Population, ages 13-16, total',
 'Population, ages 13-17, female',
 'Population, ages 13-17, male',
 'Population, ages 13-17, total',
 'Population, ages 13-18, female',
 'Population, ages 13-18, male',
 'Population, ages 13-18, total',
 'Population, ages 13-19, female',
 'Population, ages 13-19, male',
 'Population, ages 13-19, total',
 'Population, ages 14-18, female',
 'Population, ages 14-18, male',
 'Population, ages 14-18, total',
 'Population, ages 14-19, female',
 'Population, ages 14-19, male',
 'Population, ages 14-19, total',
 'Population, ages 15-24, female',
 'Population, ages 15-24, male',
 'Population, ages 15-24, total',
 'Population, age 0, male',
 'Population, age 1, male',
 'Population, age 2, male',
 'Population, age 3, male',
 'Population, age 4, male',
 'Population, age 5, male',
 'Population, age 6, male',
 'Population, age 7, male',
 'Population, age 8, male',
 'Population, age 9, male',
 'Population, age 10, male',
 'Population, age 11, male',
 'Population, age 12, male',
 'Population, age 13, male',
 'Population, age 14, male',
 'Population, age 15, male',
 'Population, age 16, male',
 'Population, age 17, male',
 'Population, age 18, male',
 'Population, age 19, male',
 'Population, age 20, male',
 'Population, age 21, male',
 'Population, age 22, male',
 'Population, age 23, male',
 'Population, age 24, male',
 'Population, age 25, male',
 'Population growth (annual %)',
 'Population, total',
 'Population, female',
 'Population, female (% of total)',
 'Population, male',
 'Population, male (% of total)',
 'Population of the official age for pre-primary education, female (number)',
 'Population of the official age for pre-primary education, both sexes (number)',
 'Population of the official age for pre-primary education, male (number)',
 'Population of the official age for the last grade of primary education, female (number)',
 'Population of the official age for the last grade of primary education, male (number)',
 'Population of the official age for the last grade of primary education, both sexes (number)',
 'Population of the official age for primary education, female (number)',
 'Population of the official age for primary education, both sexes (number)',
 'Population of the official age for primary education, male (number)',
 'Population of the official age for lower secondary education, female (number)',
 'Population of the official age for lower secondary education, both sexes (number)',
 'Population of the official age for lower secondary education, male (number)',
 'Population of the official age for secondary education, female (number)',
 'Population of the official age for secondary education, both sexes (number)',
 'Population of the official age for secondary education, male (number)',
 'Population of the official age for upper secondary education, female (number)',
 'Population of the official age for upper secondary education, both sexes (number)',
 'Population of the official age for upper secondary education, male (number)',
 'Population of the official age for tertiary education, female (number)',
 'Population of the official age for tertiary education, both sexes (number)',
 'Population of the official age for tertiary education, male (number)',
 'Primary completion rate, gender parity index (GPI)',
 'Gross intake ratio to Grade 1 of primary education, gender parity index (GPI)',
 'Gross intake ratio to Grade 1 of lower secondary general education, both sexes (%)',
 'Gross intake ratio to Grade 1 of lower secondary general education, female (%)',
 'Lower secondary completion rate, gender parity index (GPI)',
 'Gross intake ratio to Grade 1 of lower secondary general education, gender parity index (GPI)',
 'Gross intake ratio to Grade 1 of lower secondary general education, male (%)',
 'Official entrance age to compulsory education (years)',
 'Drop-out rate from Grade 1 of primary education, both sexes (%)',
 'Drop-out rate from Grade 1 of primary education, female (%)',
 'Drop-out rate from Grade 1 of primary education, male (%)',
 'Drop-out rate from Grade 2 of primary education, both sexes (%)',
 'Drop-out rate from Grade 2 of primary education, female (%)',
 'Drop-out rate from Grade 2 of primary education, male (%)',
 'Drop-out rate from Grade 3 of primary education, both sexes (%)',
 'Drop-out rate from Grade 3 of primary education, female (%)',
 'Drop-out rate from Grade 3 of primary education, male (%)',
 'Drop-out rate from Grade 4 of primary education, both sexes (%)',
 'Drop-out rate from Grade 4 of primary education, female (%)',
 'Drop-out rate from Grade 4 of primary education, male (%)',
 'Drop-out rate from Grade 5 of primary education, both sexes (%)',
 'Drop-out rate from Grade 5 of primary education, female (%)',
 'Drop-out rate from Grade 5 of primary education, male (%)',
 'Drop-out rate from Grade 6 of primary education, both sexes (%)',
 'Drop-out rate from Grade 6 of primary education, female (%)',
 'Drop-out rate from Grade 6 of primary education, male (%)',
 'Cumulative drop-out rate to the last grade of lower secondary general education, female (%)',
 'Drop-out rate from Grade 1 of lower secondary general education, female (%)',
 'Drop-out rate from Grade 1 of lower secondary general education, male (%)',
 'Drop-out rate from Grade 1 of lower secondary general education, both sexes (%)',
 'Drop-out rate from Grade 2 of lower secondary general education, female (%)',
 'Drop-out rate from Grade 2 of lower secondary general education, male (%)',
 'Drop-out rate from Grade 2 of lower secondary general education, both sexes (%)',
 'Drop-out rate from Grade 3 of lower secondary general education, female (%)',
 'Drop-out rate from Grade 3 of lower secondary general education, male (%)',
 'Drop-out rate from Grade 3 of lower secondary general education, both sexes (%)',
 'Drop-out rate from Grade 4 of lower secondary general education, female (%)',
 'Drop-out rate from Grade 4 of lower secondary general education, male (%)',
 'Drop-out rate from Grade 4 of lower secondary general education, both sexes (%)',
 'Cumulative drop-out rate to the last grade of lower secondary general education, male (%)',
 'Cumulative drop-out rate to the last grade of lower secondary general education, both sexes (%)',
 'Enrolment in early childhood education, female (number)',
 'Enrolment in early childhood education, private institutions, female (number)',
 'Enrolment in early childhood education, private institutions, both sexes (number)',
 'Enrolment in early childhood education, public institutions, female (number)',
 'Enrolment in early childhood education, public institutions, both sexes (number)',
 'Enrolment in early childhood education, both sexes (number)',
 'Enrolment in early childhood educational development programmes, female (number)',
 'Enrolment in early childhood educational development programmes, private institutions, female (number)',
 'Enrolment in early childhood educational development programmes, private institutions, both sexes (number)',
 'Enrolment in early childhood educational development programmes, public institutions, female (number)',
 'Enrolment in early childhood educational development programmes, public institutions, both sexes (number)',
 'Enrolment in early childhood educational development programmes, both sexes (number)',
 'Enrolment in pre-primary education, private institutions, both sexes (number)',
 'Enrolment in pre-primary education, private institutions, female (number)',
 'Enrolment in pre-primary education, public institutions, both sexes (number)',
 'Enrolment in pre-primary education, public institutions, female (number)',
 'Enrolment in Grade 1 of primary education, both sexes (number)',
 'Enrolment in Grade 1 of primary education, female (number)',
 'Enrolment in Grade 2 of primary education, both sexes (number)',
 'Enrolment in Grade 2 of primary education, female (number)',
 'Enrolment in Grade 3 of primary education, both sexes (number)',
 'Enrolment in Grade 3 of primary education, female (number)',
 'Enrolment in Grade 4 of primary education, both sexes (number)',
 'Enrolment in Grade 4 of primary education, female (number)',
 'Enrolment in Grade 5 of primary education, both sexes (number)',
 'Enrolment in Grade 5 of primary education, female (number)',
 'Enrolment in Grade 6 of primary education, both sexes (number)',
 'Enrolment in Grade 6 of primary education, female (number)',
 'Enrolment in Grade 7 of primary education, both sexes (number)',
 'Enrolment in Grade 7 of primary education, female (number)',
 'Enrolment in primary education, Grade unspecified, both sexes (number)',
 'Enrolment in primary education, Grade unspecified, female (number)',
 'Enrolment in primary education, private institutions, both sexes (number)',
 'Enrolment in primary education, private institutions, female (number)',
 'Enrolment in primary education, public institutions, both sexes (number)',
 'Enrolment in primary education, public institutions, female (number)',
 'Enrolment in lower secondary education, both sexes (number)',
 'Enrolment in lower secondary education, female (number)',
 'Enrolment in lower secondary general, both sexes (number)',
 'Enrolment in lower secondary general, female (number)',
 'Enrolment in Grade 1 of lower secondary general education, female (number)',
 'Enrolment in Grade 1 of lower secondary general education, both sexes (number)',
 'Enrolment in Grade 2 of lower secondary general education, female (number)',
 'Enrolment in Grade 2 of lower secondary general education, both sexes (number)',
 'Enrolment in Grade 3 of lower secondary general education, female (number)',
 'Enrolment in Grade 3 of lower secondary general education, both sexes (number)',
 'Enrolment in Grade 4 of lower secondary general education, female (number)',
 'Enrolment in Grade 4 of lower secondary general education, both sexes (number)',
 'Enrolment in Grade 5 of lower secondary general education, female (number)',
 'Enrolment in Grade 5 of lower secondary general education, both sexes (number)',
 'Enrolment in lower secondary general education, Grade unspecified, female (number)',
 'Enrolment in lower secondary general education, Grade unspecified, both sexes (number)',
 'Enrolment in lower secondary education, private institutions, both sexes (number)',
 'Enrolment in lower secondary education, private institutions, female (number)',
 'Enrolment in lower secondary education, public institutions, both sexes (number)',
 'Enrolment in lower secondary education, public institutions, female (number)',
 'Enrolment in lower secondary vocational, both sexes (number)',
 'Enrolment in lower secondary vocational, female (number)',
 'Enrolment in secondary education, private institutions, both sexes (number)',
 'Enrolment in secondary education, private institutions, female (number)',
 'Enrolment in secondary education, public institutions, both sexes (number)',
 'Enrolment in secondary education, public institutions, female (number)',
 'Enrolment in upper secondary education, both sexes (number)',
 'Enrolment in upper secondary education, female (number)',
 'Enrolment in upper secondary general, both sexes (number)',
 'Enrolment in upper secondary general, female (number)',
 'Enrolment in upper secondary education, private institutions, both sexes (number)',
 'Enrolment in upper secondary education, private institutions, female (number)',
 'Enrolment in upper secondary education, public institutions, both sexes (number)',
 'Enrolment in upper secondary education, public institutions, female (number)',
 'Enrolment in upper secondary vocational, both sexes (number)',
 'Enrolment in upper secondary vocational, female (number)',
 'Enrolment in post-secondary non-tertiary education, both sexes (number)',
 'Enrolment in post-secondary non-tertiary education, female (number)',
 'Enrolment in post-secondary non-tertiary education, private institutions, both sexes (number)',
 'Enrolment in post-secondary non-tertiary education, private institutions, female (number)',
 'Enrolment in post-secondary non-tertiary education, public institutions, both sexes (number)',
 'Enrolment in post-secondary non-tertiary education, public institutions, female (number)',
 'Enrolment in tertiary education, ISCED 5 programmes, both sexes (number)',
 'Enrolment in tertiary education, ISCED 5 programmes, female (number)',
 'Enrolment in tertiary education, ISCED 6 programmes, both sexes (number)',
 'Enrolment in tertiary education, ISCED 6 programmes, female (number)',
 'Enrolment in tertiary education, ISCED 8 programmes, both sexes (number)',
 'Enrolment in tertiary education, ISCED 8 programmes, female (number)',
 'UIS: Percentage of population age 25+ with completed primary education. Total',
 'UIS: Percentage of population age 25+ with completed primary education. Female',
 'UIS: Percentage of population age 25+ with completed primary education. Male',
 'UIS: Percentage of population age 25+ with at least completed primary education (ISCED 1 or higher). Total',
 'UIS: Percentage of population age 25+ with at least completed primary education (ISCED 1 or higher). Female',
 'UIS: Percentage of population age 25+ with at least completed primary education (ISCED 1 or higher). Gender Parity Index',
 'UIS: Percentage of population age 25+ with at least completed primary education (ISCED 1 or higher). Male',
 'UIS: Percentage of population age 25+ with completed lower secondary education. Total',
 'UIS: Percentage of population age 25+ with completed lower secondary education. Female',
 'UIS: Percentage of population age 25+ with completed lower secondary education. Male',
 'UIS: Percentage of population age 25+ with at least completed lower secondary education (ISCED 2 or higher). Total',
 'UIS: Percentage of population age 25+ with at least completed lower secondary education (ISCED 2 or higher). Female',
 'UIS: Percentage of population age 25+ with at least completed lower secondary education (ISCED 2 or higher). Gender Parity Index',
 'UIS: Percentage of population age 25+ with at least completed lower secondary education (ISCED 2 or higher). Male',
 'UIS: Percentage of population age 25+ with completed upper secondary education. Total',
 'UIS: Percentage of population age 25+ with completed upper secondary education. Female',
 'UIS: Percentage of population age 25+ with completed upper secondary education. Male',
 'UIS: Percentage of population age 25+ with at least completed upper secondary education (ISCED 3 or higher). Total',
 'UIS: Percentage of population age 25+ with at least completed upper secondary education (ISCED 3 or higher). Female',
 'UIS: Percentage of population age 25+ with at least completed upper secondary education (ISCED 3 or higher). Gender Parity Index',
 'UIS: Percentage of population age 25+ with at least completed upper secondary education (ISCED 3 or higher). Male',
 'UIS: Percentage of population age 25+ with completed post-secondary education. Total',
 'UIS: Percentage of population age 25+ with completed post-secondary education. Female',
 'UIS: Percentage of population age 25+ with completed post-secondary education. Male',
 'UIS: Percentage of population age 25+ with at least completed post-secondary education (ISCED 4 or higher). Total',
 'UIS: Percentage of population age 25+ with at least completed post-secondary education (ISCED 4 or higher). Female',
 'UIS: Percentage of population age 25+ with at least completed post-secondary education (ISCED 4 or higher). Gender Parity Index',
 'UIS: Percentage of population age 25+ with at least completed post-secondary education (ISCED 4 or higher). Male',
 'UIS: Percentage of population age 25+ with at least a completed short-cycle tertiary degree (ISCED 5 or higher). Total',
 'UIS: Percentage of population age 25+ with at least a completed short-cycle tertiary degree (ISCED 5 or higher). Female',
 'UIS: Percentage of population age 25+ with at least a completed short-cycle tertiary degree (ISCED 5 or higher). Gender Parity Index',
 'UIS: Percentage of population age 25+ with at least a completed short-cycle tertiary degree (ISCED 5 or higher). Male',
 "UIS: Percentage of population age 25+ with a completed bachelor's or equivalent degree (ISCED 6). Total",
 "UIS: Percentage of population age 25+ with a completed bachelor's or equivalent degree (ISCED 6). Female",
 "UIS: Percentage of population age 25+ with a completed bachelor's or equivalent degree (ISCED 6). Male",
 'UIS: Mean years of schooling of the population age 25+. Total',
 'UIS: Mean years of schooling of the population age 25+. Female',
 'UIS: Mean years of schooling of the population age 25+. Male',
 'UIS: Percentage of population age 25+ with no schooling. Total',
 'UIS: Percentage of population age 25+ with no schooling. Female',
 'UIS: Percentage of population age 25+ with no schooling. Male',
 'UIS: Percentage of population age 25+ with some primary education. Total',
 'UIS: Percentage of population age 25+ with some primary education. Female',
 'UIS: Percentage of population age 25+ with some primary education. Male',
 'UIS: Percentage of population age 25+ with unknown educational attainment. Total',
 'UIS: Percentage of population age 25+ with unknown educational attainment. Female',
 'UIS: Percentage of population age 25+ with unknown educational attainment. Male',
 'Percentage of new entrants to Grade 1 of primary education with early childhood educational experience, both sexes (%)',
 'Percentage of new entrants to Grade 1 of primary education with early childhood educational experience, female (%)',
 'Percentage of new entrants to Grade 1 of primary education with early childhood educational experience, gender parity index (GPI)',
 'Percentage of new entrants to Grade 1 of primary education with early childhood educational experience, male (%)',
 'Early school leavers from primary education, female (number)',
 'Early school leavers from primary education, male (number)',
 'Early school leavers from primary education, both sexes (number)',
 'Percentage of students in early childhood education who are female (%)',
 'Percentage of students in early childhood educational development programmes who are female (%)',
 'Percentage of students in lower secondary education who are female (%)',
 'Percentage of students in lower secondary general education who are female (%)',
 'Percentage of students in lower secondary vocational education who are female (%)',
 'Percentage of students in upper secondary education who are female (%)',
 'Percentage of students in upper secondary general education who are female (%)',
 'Percentage of students in upper secondary vocational education who are female (%)',
 'Percentage of students in post-secondary non-tertiary education who are female (%)',
 'Percentage of students in post-secondary non-tertiary general education who are female (%)',
 'Percentage of students in post-secondary non-tertiary vocational education who are female (%)',
 'Percentage of students in tertiary ISCED 5 programmes who are female (%)',
 'Percentage of students enrolled in Education programmes in tertiary education who are female (%)',
 'Percentage of students enrolled in Humanities and Arts programmes in tertiary education who are female (%)',
 'Percentage of students enrolled in Social Sciences, Business and Law programmes in tertiary education who are female (%)',
 'Percentage of students enrolled in Science programmes in tertiary education who are female (%)',
 'Percentage of students enrolled in Engineering, Manufacturing and Construction programmes in tertiary education who are female (%)',
 'Percentage of students enrolled in Agriculture programmes in tertiary education who are female (%)',
 'Percentage of students enrolled in Health and Welfare programmes in tertiary education who are female (%)',
 'Percentage of students enrolled in Services programmes in tertiary education who are female (%)',
 'Percentage of all inbound internationally mobile students in tertiary education in the host country who are female, (%)',
 'Percentage of students enrolled in programmes in unspecified fields in tertiary education who are female (%)',
 'Percentage of students in tertiary ISCED 6 programmes who are female (%)',
 'Percentage of students in tertiary ISCED 8 programmes who are female (%)',
 'Percentage of graduates from tertiary ISCED 5 programmes who are female (%)',
 'Percentage of graduates from Science and Technology programmes in tertiary education who are female (%)',
 'Percentage of graduates from non-science and non-technology fields in tertiary education who are female (%)',
 'Percentage of graduates from tertiary ISCED 6 programmes who are female (%)',
 'Percentage of graduates from tertiary ISCED 8 programmes who are female (%)',
 'New entrants to Grade 1 of primary education, percentage female (%)',
 'Out-of-school children of primary school age, percentage female (%)',
 'Out-of-school adolescents of lower secondary school age, percentage female (%)',
 'Out-of-school youth of upper secondary school age, percentage female (%)',
 'Percentage of students in tertiary education enrolled in Education programmes, both sexes (%)',
 'Percentage of female students in tertiary education enrolled in Education programmes, female (%)',
 'Percentage of male students in tertiary education enrolled in Education programmes, male (%)',
 'Percentage of students in tertiary education enrolled in Humanities and Arts programmes, both sexes (%)',
 'Percentage of female students in tertiary education enrolled in Humanities and Arts programmes, female (%)',
 'Percentage of male students in tertiary education enrolled in Humanities and Arts programmes, male (%)',
 'Percentage of students in tertiary education enrolled in Social Sciences, Business and Law programmes, both sexes (%)',
 'Percentage of female students in tertiary education enrolled in Social Sciences, Business and Law programmes, female (%)',
 'Percentage of male students in tertiary education enrolled in Social Sciences, Business and Law programmes, male (%)',
 'Percentage of students in tertiary education enrolled in Science programmes, both sexes (%)',
 'Percentage of female students in tertiary education enrolled in Science programmes, female (%)',
 'Percentage of male students in tertiary education enrolled in Science programmes, male (%)',
 'Percentage of students in tertiary education enrolled in Engineering, Manufacturing and Construction programmes, both sexes (%)',
 'Percentage of female students in tertiary education enrolled in Engineering, Manufacturing and Construction programmes, female (%)',
 'Percentage of male students in tertiary education enrolled in Engineering, Manufacturing and Construction programmes, male (%)',
 'Percentage of students in tertiary education enrolled in Agriculture programmes, both sexes (%)',
 'Percentage of female students in tertiary education enrolled in Agriculture programmes, female (%)',
 'Percentage of male students in tertiary education enrolled in Agriculture programmes, male (%)',
 'Percentage of students in tertiary education enrolled in Health and Welfare programmes, both sexes (%)',
 'Percentage of female students in tertiary education enrolled in Health and Welfare programmes, female (%)',
 'Percentage of male students in tertiary education enrolled in Health and Welfare programmes, male (%)',
 'Percentage of students in tertiary education enrolled in Services programmes, both sexes (%)',
 'Percentage of female students in tertiary education enrolled in Services programmes, female (%)',
 'Percentage of male students in tertiary education enrolled in Services programmes, male (%)',
 'Percentage of students in tertiary education enrolled in programmes in unspecified fields, both sexes (%)',
 'Percentage of female students in tertiary education enrolled in programmes in unspecified fields, female (%)',
 'Percentage of male students in tertiary education enrolled in programmes in unspecified fields, male (%)',
 'Percentage of male graduates from tertiary education graduating from Education programmes, male (%)',
 'Percentage of male graduates from tertiary education graduating from Humanities and Arts programmes, male (%)',
 'Percentage of male graduates from tertiary education graduating from Social Sciences, Business and Law programmes, male (%)',
 'Percentage of male graduates from tertiary education graduating from Science programmes, male (%)',
 'Percentage of male graduates from tertiary education graduating from Engineering, Manufacturing and Construction programmes, male (%)',
 'Percentage of male graduates from tertiary education graduating from Agriculture programmes, male (%)',
 'Percentage of male graduates from tertiary education graduating from Health and Welfare programmes, male (%)',
 'Percentage of male graduates from tertiary education graduating from Services programmes, male (%)',
 'Percentage of male graduates from tertiary education graduating from programmes in unspecified fields, male (%)',
 'Percentage of teachers in lower secondary education who are female (%)',
 'Percentage of teachers in upper secondary education who are female (%)',
 'Percentage of teachers in post-secondary non-tertiary education who are female (%)',
 'Graduates from ISCED 5 programmes in tertiary education, female (number)',
 'Graduates from ISCED 5 programmes in tertiary education, both sexes (number)',
 'Graduates from ISCED 6 programmes in tertiary education, female (number)',
 'Graduates from ISCED 6 programmes in tertiary education, both sexes (number)',
 'Graduates from ISCED 8 programmes in tertiary education, female (number)',
 'Graduates from ISCED 8 programmes in tertiary education, both sexes (number)',
 'Gross enrolment ratio, pre-primary, gender parity index (GPI)',
 'Gross enrolment ratio, primary and lower secondary, both sexes (%)',
 'Gross enrolment ratio, primary and lower secondary, female (%)',
 'Gross enrolment ratio, primary and lower secondary, gender parity index (GPI)',
 'Gross enrolment ratio, primary and lower secondary, male (%)',
 'Gross enrolment ratio, primary and secondary, both sexes (%)',
 'Gross enrolment ratio, primary and secondary, female (%)',
 'Gross enrolment ratio, primary and secondary, male (%)',
 'Gross enrolment ratio, primary to tertiary, female (%)',
 'Gross enrolment ratio, primary to tertiary, gender parity index (GPI)',
 'Gross enrolment ratio, primary to tertiary, male (%)',
 'Gross enrolment ratio, lower secondary, gender parity index (GPI)',
 'Gross enrolment ratio, upper secondary, gender parity index (GPI)',
 'Gross enrolment ratio, post-secondary non-tertiary, both sexes (%)',
 'Gross enrolment ratio, post-secondary non-tertiary, female (%)',
 'Gross enrolment ratio, post-secondary non-tertiary, gender parity index (GPI)',
 'Gross enrolment ratio, post-secondary non-tertiary, male (%)',
 'Gross graduation ratio from primary education, gender parity index (GPI)',
 'Gross graduation ratio from lower secondary education, both sexes (%)',
 'Gross graduation ratio from lower secondary education, female (%)',
 'Gross graduation ratio from lower secondary education, gender parity index (GPI)',
 'Gross graduation ratio from lower secondary education, male (%)',
 'Gross graduation ratio from first degree programmes (ISCED 6 and 7) in tertiary education, gender parity index (GPI)',
 'Gross outbound enrolment ratio, all regions, both sexes (%)',
 'Percentage of students in lower secondary education enrolled in general programmes, both sexes (%)',
 'Percentage of female students in lower secondary education enrolled in general programmes, female (%)',
 'Percentage of male students in lower secondary education enrolled in general programmes, male (%)',
 'Percentage of students in lower secondary education enrolled in vocational programmes, both sexes (%)',
 'Percentage of female students in lower secondary education enrolled in vocational programmes, female (%)',
 'Percentage of male students in lower secondary education enrolled in vocational programmes, male (%)',
 'Percentage of students in secondary education enrolled in general programmes, both sexes (%)',
 'Percentage of female students in secondary education enrolled in general programmes, female (%)',
 'Percentage of male students in secondary education enrolled in general programmes, male (%)',
 'Percentage of students in upper secondary education enrolled in general programmes, both sexes (%)',
 'Percentage of female students in upper secondary education enrolled in general programmes, female (%)',
 'Percentage of male students in upper secondary education enrolled in general programmes, male (%)',
 'Percentage of students in upper secondary education enrolled in vocational programmes, both sexes (%)',
 'Percentage of female students in upper secondary education enrolled in vocational programmes, female (%)',
 'Percentage of male students in upper secondary education enrolled in vocational programmes, male (%)',
 'Percentage of students in post-secondary non-tertiary education enrolled in general programmes, both sexes (%)',
 'Percentage of female students in post-secondary non-tertiary education enrolled in general programmes, female (%)',
 'Percentage of male students in post-secondary non-tertiary education enrolled in general programmes, male (%)',
 'Percentage of students in post-secondary non-tertiary education enrolled in vocational programmes, both sexes (%)',
 'Percentage of female students in post-secondary non-tertiary education enrolled in vocational programmes, female (%)',
 'Percentage of male students in post-secondary non-tertiary education enrolled in vocational programmes, male (%)',
 'Illiterate population, 25-64 years, both sexes (number)',
 'Illiterate population, 25-64 years, female (number)',
 'Illiterate population, 25-64 years, male (number)',
 'Illiterate population, 25-64 years, % female',
 'Youth illiterate population, 15-24 years, both sexes (number)',
 'Youth illiterate population, 15-24 years, female (number)',
 'Youth illiterate population, 15-24 years, male (number)',
 'Adult illiterate population, 15+ years, both sexes (number)',
 'Adult illiterate population, 15+ years, female (number)',
 'Adult illiterate population, 15+ years, male (number)',
 'Elderly illiterate population, 65+ years, both sexes (number)',
 'Elderly illiterate population, 65+ years, female (number)',
 'Elderly illiterate population, 65+ years, male (number)',
 'Youth illiterate population, 15-24 years, % female',
 'Adult illiterate population, 15+ years, % female',
 'Elderly illiterate population, 65+ years, % female',
 'Adult literacy rate, population 15+ years, gender parity index (GPI)',
 'Literacy rate, population 25-64 years, both sexes (%)',
 'Literacy rate, population 25-64 years, female (%)',
 'Literacy rate, population 25-64 years, gender parity index (GPI)',
 'Literacy rate, population 25-64 years, male (%)',
 'Elderly literacy rate, population 65+ years, both sexes (%)',
 'Elderly literacy rate, population 65+ years, female (%)',
 'Elderly literacy rate, population 65+ years, gender parity index (GPI)',
 'Elderly literacy rate, population 65+ years, male (%)',
 'Net flow of internationally mobile students (inbound - outbound), both sexes (number)',
 'Net flow ratio of internationally mobile students (inbound - outbound), both sexes (%)',
 'Total inbound internationally mobile students, female (number)',
 'Total inbound internationally mobile students, both sexes (number)',
 'Inbound mobility rate, both sexes (%)',
 'Inbound mobility rate, female (%)',
 'Inbound mobility rate, male (%)',
 'New entrants to Grade 1 of primary education with early childhood education experience , both sexes (number)',
 'New entrants to Grade 1 of primary education with early childhood education experience, female (number)',
 'New entrants to Grade 1 of primary education, both sexes (number)',
 'New entrants to Grade 1 of primary education, female (number)',
 'Net enrolment rate, pre-primary, gender parity index (GPI)',
 'Net enrolment rate, primary, gender parity index (GPI)',
 'Net enrolment rate, lower secondary, both sexes (%)',
 'Net enrolment rate, lower secondary, female (%)',
 'Net enrolment rate, lower secondary, male (%)',
 'Net enrolment rate, secondary, gender parity index (GPI)',
 'Net enrolment rate, upper secondary, both sexes (%)',
 'Net enrolment rate, upper secondary, female (%)',
 'Net enrolment rate, upper secondary, male (%)',
 'Adjusted net enrolment rate, primary, gender parity index (GPI)',
 'Adjusted net enrolment rate, lower secondary, both sexes (%)',
 'Adjusted net enrolment rate, lower secondary, female (%)',
 'Adjusted net enrolment rate, lower secondary, gender parity index (GPI)',
 'Adjusted net enrolment rate, lower secondary, male (%)',
 'Adjusted net enrolment rate, upper secondary, both sexes (%)',
 'Adjusted net enrolment rate, upper secondary, female (%)',
 'Adjusted net enrolment rate, upper secondary, gender parity index (GPI)',
 'Adjusted net enrolment rate, upper secondary, male (%)',
 'Total net enrolment rate, primary, both sexes (%)',
 'Total net enrolment rate, primary, female (%)',
 'Total net enrolment rate, primary, gender parity index (GPI)',
 'Total net enrolment rate, primary, male (%)',
 'Total net enrolment rate, lower secondary, both sexes (%)',
 'Total net enrolment rate, lower secondary, female (%)',
 'Total net enrolment rate, lower secondary, gender parity index (GPI)',
 'Total net enrolment rate, lower secondary, male (%)',
 'Net intake rate to Grade 1 of primary education by under-age entrants (-1 year), both sexes (%)',
 'Net intake rate to Grade 1 of primary education by under-age entrants (-1 year), female (%)',
 'Net intake rate to Grade 1 of primary education by under-age entrants (-1 year), gender parity index (GPI)',
 'Net intake rate to Grade 1 of primary education by under-age entrants (-1 year), male (%)',
 'Net intake rate to Grade 1 of primary education by over-age entrants (+1 year), both sexes (%)',
 'Net intake rate to Grade 1 of primary education by over-age entrants (+1 year), female (%)',
 'Net intake rate to Grade 1 of primary education by over-age entrants (+1 year), gender parity index (GPI)',
 'Net intake rate to Grade 1 of primary education by over-age entrants (+1 year), male (%)',
 'Net intake rate to Grade 1 of primary education, gender parity index (GPI)',
 'Adjusted net intake rate to Grade 1 of primary education, both sexes (%)',
 'Adjusted net intake rate to Grade 1 of primary education, female (%)',
 'Adjusted net intake rate to Grade 1 of primary education, gender parity index (GPI)',
 'Adjusted net intake rate to Grade 1 of primary education, male (%)',
 'Percentage of students enrolled in primary education who are over-age, both sexes (%)',
 'Percentage of female students enrolled in primary education who are over-age, female (%)',
 'Percentage of male students enrolled in primary education who are over-age, male (%)',
 'Over-age enrolment ratio in primary education, both sexes (%)',
 'Over-age enrolment ratio in primary education, female (%)',
 'Over-age enrolment ratio in primary education, male (%)',
 'Total outbound internationally mobile tertiary students studying abroad, all countries, both sexes (number)',
 'Out-of-school children of primary school age in pre-primary education, both sexes (number)',
 'Out-of-school children of primary school age in pre-primary education, female (number)',
 'Out-of-school children of primary school age in pre-primary education, male (number)',
 'Out-of-school adolescents of lower secondary school age, both sexes (number)',
 'Out-of-school adolescents of lower secondary school age, female (number)',
 'Out-of-school adolescents of lower secondary school age, male (number)',
 'Out-of-school youth of upper secondary school age, both sexes (number)',
 'Out-of-school youth of upper secondary school age, female (number)',
 'Out-of-school youth of upper secondary school age, male (number)',
 'Outbound mobility ratio, all regions, both sexes (%)',
 'Percentage of enrolment in early childhood education programmes in private institutions (%)',
 'Percentage of enrolment in early childhood educational development programmes in private institutions (%)',
 'Percentage of enrolment in lower secondary education in private institutions (%)',
 'Percentage of enrolment in upper secondary education in private institutions (%)',
 'Percentage of enrolment in post-secondary non-tertiary education in private institutions (%)',
 'Pupil/trained teacher ratio in pre-primary education (headcount basis)',
 'Pupil/trained teacher ratio in primary education (headcount basis)',
 'Pupil-teacher ratio in lower secondary education (headcount basis)',
 'Pupil/trained teacher ratio in lower secondary education (headcount basis)',
 'Pupil/trained teacher ratio in secondary education (headcount basis)',
 'Pupil-teacher ratio in upper secondary education (headcount basis)',
 'Pupil/trained teacher ratio in upper secondary education (headcount basis)',
 'Pupil-teacher ratio in tertiary education (headcount basis)',
 'Repeaters in primary education, all grades, both sexes (number)',
 'Repeaters in primary education, all grades, female (number)',
 'Repeaters in Grade 1 of primary education, both sexes (number)',
 'Repeaters in Grade 1 of primary education, female (number)',
 'Repeaters in Grade 2 of primary education, both sexes (number)',
 'Repeaters in Grade 2 of primary education, female (number)',
 'Repeaters in Grade 3 of primary education, both sexes (number)',
 'Repeaters in Grade 3 of primary education, female (number)',
 'Repeaters in Grade 4 of primary education, both sexes (number)',
 'Repeaters in Grade 4 of primary education, female (number)',
 'Repeaters in Grade 5 of primary education, both sexes (number)',
 'Repeaters in Grade 5 of primary education, female (number)',
 'Repeaters in Grade 6 of primary education, both sexes (number)',
 'Repeaters in Grade 6 of primary education, female (number)',
 'Repeaters in Grade 7 of primary education, both sexes (number)',
 'Repeaters in Grade 7 of primary education, female (number)',
 'Repeaters in lower secondary general education, all grades, both sexes (number)',
 'Repeaters in lower secondary general education, all grades, female (number)',
 'Repeaters in Grade 1 of lower secondary general education, both sexes (number)',
 'Repeaters in Grade 1 of lower secondary general education, female (number)',
 'Repeaters in Grade 2 of lower secondary general education, both sexes (number)',
 'Repeaters in Grade 2 of lower secondary general education, female (number)',
 'Repeaters in Grade 3 of lower secondary general education, both sexes (number)',
 'Repeaters in Grade 3 of lower secondary general education, female (number)',
 'Repeaters in Grade 4 of lower secondary general education, both sexes (number)',
 'Repeaters in Grade 4 of lower secondary general education, female (number)',
 'Repeaters in Grade 5 of lower secondary general education, both sexes (number)',
 'Repeaters in Grade 5 of lower secondary general education, female (number)',
 'Percentage of repeaters in Grade 1 of primary education, both sexes (%)',
 'Percentage of repeaters in Grade 1 of primary education, female (%)',
 'Percentage of repeaters in Grade 1 of primary education, male (%)',
 'Percentage of repeaters in Grade 2 of primary education, both sexes (%)',
 'Percentage of repeaters in Grade 2 of primary education, female (%)',
 'Percentage of repeaters in Grade 2 of primary education, male (%)',
 'Percentage of repeaters in Grade 3 of primary education, both sexes (%)',
 'Percentage of repeaters in Grade 3 of primary education, female (%)',
 'Percentage of repeaters in Grade 3 of primary education, male (%)',
 'Percentage of repeaters in Grade 4 of primary education, both sexes (%)',
 'Percentage of repeaters in Grade 4 of primary education, female (%)',
 'Percentage of repeaters in Grade 4 of primary education, male (%)',
 'Percentage of repeaters in Grade 5 of primary education, both sexes (%)',
 'Percentage of repeaters in Grade 5 of primary education, female (%)',
 'Percentage of repeaters in Grade 5 of primary education, male (%)',
 'Percentage of repeaters in Grade 6 of primary education, both sexes (%)',
 'Percentage of repeaters in Grade 6 of primary education, female (%)',
 'Percentage of repeaters in Grade 6 of primary education, male (%)',
 'Percentage of repeaters in Grade 7 of primary education, both sexes (%)',
 'Percentage of repeaters in Grade 7 of primary education, female (%)',
 'Percentage of repeaters in Grade 7 of primary education, male (%)',
 'Percentage of repeaters in primary education, all grades, gender parity index (GPI)',
 'Percentage of repeaters in lower secondary general education, all grades, both sexes (%)',
 'Percentage of repeaters in lower secondary general education, all grades, female (%)',
 'Percentage of repeaters in Grade 1 of lower secondary general education, both sexes (%)',
 'Percentage of repeaters in Grade 1 of lower secondary general education, female (%)',
 'Percentage of repeaters in Grade 1 of lower secondary general education, male (%)',
 'Percentage of repeaters in Grade 2 of lower secondary general education, both sexes (%)',
 'Percentage of repeaters in Grade 2 of lower secondary general education, female (%)',
 'Percentage of repeaters in Grade 2 of lower secondary general education, male (%)',
 'Percentage of repeaters in Grade 3 of lower secondary general education, both sexes (%)',
 'Percentage of repeaters in Grade 3 of lower secondary general education, female (%)',
 'Percentage of repeaters in Grade 3 of lower secondary general education, male (%)',
 'Percentage of repeaters in Grade 4 of lower secondary general education, both sexes (%)',
 'Percentage of repeaters in Grade 4 of lower secondary general education, female (%)',
 'Percentage of repeaters in Grade 4 of lower secondary general education, male (%)',
 'Percentage of repeaters in Grade 5 of lower secondary general education, both sexes (%)',
 'Percentage of repeaters in Grade 5 of lower secondary general education, female (%)',
 'Percentage of repeaters in Grade 5 of lower secondary general education, male (%)',
 'Percentage of repeaters in lower secondary education, all grades, gender parity index (GPI)',
 'Percentage of repeaters in lower secondary general education, all grades, male (%)',
 'Repetition rate in primary education (all grades), both sexes (%)',
 'Repetition rate in primary education (all grades), female (%)',
 'Repetition rate in Grade 1 of primary education, both sexes (%)',
 'Repetition rate in Grade 1 of primary education, female (%)',
 'Repetition rate in Grade 1 of primary education, male (%)',
 'Repetition rate in Grade 2 of primary education, both sexes (%)',
 'Repetition rate in Grade 2 of primary education, female (%)',
 'Repetition rate in Grade 2 of primary education, male (%)',
 'Repetition rate in Grade 3 of primary education, both sexes (%)',
 'Repetition rate in Grade 3 of primary education, female (%)',
 'Repetition rate in Grade 3 of primary education, male (%)',
 'Repetition rate in Grade 4 of primary education, both sexes (%)',
 'Repetition rate in Grade 4 of primary education, female (%)',
 'Repetition rate in Grade 4 of primary education, male (%)',
 'Repetition rate in Grade 5 of primary education, both sexes (%)',
 'Repetition rate in Grade 5 of primary education, female (%)',
 'Repetition rate in Grade 5 of primary education, male (%)',
 'Repetition rate in Grade 6 of primary education, both sexes (%)',
 'Repetition rate in Grade 6 of primary education, female (%)',
 'Repetition rate in Grade 6 of primary education, male (%)',
 'Repetition rate in Grade 7 of primary education, both sexes (%)',
 'Repetition rate in Grade 7 of primary education, female (%)',
 'Repetition rate in Grade 7 of primary education, male (%)',
 'Repetition rate in primary education (all grades), male (%)',
 'Repetition rate in lower secondary general education (all grades), both sexes (%)',
 'Repetition rate in lower secondary general education (all grades), female (%)',
 'Repetition rate in Grade 1 of lower secondary general education, both sexes (%)',
 'Repetition rate in Grade 1 of lower secondary general education, female (%)',
 'Repetition rate in Grade 1 of lower secondary general education, male (%)',
 'Repetition rate in Grade 2 of lower secondary general education, both sexes (%)',
 'Repetition rate in Grade 2 of lower secondary general education, female (%)',
 'Repetition rate in Grade 2 of lower secondary general education, male (%)',
 'Repetition rate in Grade 3 of lower secondary general education, both sexes (%)',
 'Repetition rate in Grade 3 of lower secondary general education, female (%)',
 'Repetition rate in Grade 3 of lower secondary general education, male (%)',
 'Repetition rate in Grade 4 of lower secondary general education, both sexes (%)',
 'Repetition rate in Grade 4 of lower secondary general education, female (%)',
 'Repetition rate in Grade 4 of lower secondary general education, male (%)',
 'Repetition rate in Grade 5 of lower secondary general education, both sexes (%)',
 'Repetition rate in Grade 5 of lower secondary general education, female (%)',
 'Repetition rate in Grade 5 of lower secondary general education, male (%)',
 'Repetition rate in lower secondary general education (all grades), male (%)',
 'Rate of out-of-school children of primary school age in pre-primary education, both sexes (%)',
 'Rate of out-of-school children of primary school age in pre-primary education, female (%)',
 'Rate of out-of-school children of primary school age in pre-primary education, male (%)',
 'Rate of out-of-school children of primary school age, both sexes (%)',
 'Rate of out-of-school children of primary school age, female (%)',
 'Rate of out-of-school children of primary school age, male (%)',
 'Rate of out-of-school adolescents of lower secondary school age, both sexes (%)',
 'Rate of out-of-school adolescents of lower secondary school age, female (%)',
 'Rate of out-of-school adolescents of lower secondary school age, male (%)',
 'Rate of out-of-school youth of upper secondary school age, both sexes (%)',
 'Rate of out-of-school youth of upper secondary school age, female (%)',
 'Rate of out-of-school youth of upper secondary school age, male (%)',
 'Population of the official entrance age to primary education, both sexes (number)',
 'Population of the official entrance age to primary education, female (number)',
 'Population of the official entrance age to primary education, male (number)',
 'Population of the official entrance age to secondary general education, both sexes (number)',
 'Population of the official entrance age to secondary general education, female (number)',
 'Population of the official entrance age to secondary general education, male (number)',
 'Population of the official age for post-secondary non-tertiary education, both sexes (number)',
 'Population of the official age for post-secondary non-tertiary education, female (number)',
 'Population of the official age for post-secondary non-tertiary education, male (number)',
 'Population of compulsory school age, both sexes (number)',
 'Population of compulsory school age, female (number)',
 'Population of compulsory school age, male (number)',
 'School life expectancy, pre-primary, both sexes (years)',
 'School life expectancy, pre-primary, female (years)',
 'School life expectancy, pre-primary, gender parity index (GPI)',
 'School life expectancy, pre-primary, male (years)',
 'School life expectancy, primary, both sexes (years)',
 'School life expectancy, primary, female (years)',
 'School life expectancy, primary, gender parity index (GPI)',
 'School life expectancy, primary, male (years)',
 'School life expectancy, primary and lower secondary, both sexes (years)',
 'School life expectancy, primary and lower secondary, female (years)',
 'School life expectancy, primary and lower secondary, male (years)',
 'School life expectancy, primary and secondary, both sexes (years)',
 'School life expectancy, primary and secondary, female (years)',
 'School life expectancy, primary and secondary, gender parity index (GPI)',
 'School life expectancy, primary and secondary, male (years)',
 'School life expectancy, primary to tertiary, gender parity index (GPI)',
 'School life expectancy, secondary, both sexes (years)',
 'School life expectancy, secondary, female (years)',
 'School life expectancy, secondary, gender parity index (GPI)',
 'School life expectancy, secondary, male (years)',
 'School life expectancy, post-secondary non-tertiary, both sexes (years)',
 'School life expectancy, post-secondary non-tertiary, female (years)',
 'School life expectancy, post-secondary non-tertiary, gender parity index (GPI)',
 'School life expectancy, post-secondary non-tertiary, male (years)',
 'School life expectancy, tertiary, both sexes (years)',
 'School life expectancy, tertiary, female (years)',
 'School life expectancy, tertiary, gender parity index (GPI)',
 'School life expectancy, tertiary, male (years)',
 'School life expectancy, primary and lower secondary (excluding repetition), female (years)',
 'School life expectancy, primary and lower secondary (excluding repetition), gender parity index (GPI)',
 'School life expectancy, primary and lower secondary (excluding repetition), male (years)',
 'School life expectancy, primary and lower secondary (excluding repetition), both sexes (years)',
 'Survival rate to Grade 4 of primary education, both sexes (%)',
 'Survival rate to Grade 4 of primary education, female (%)',
 'Survival rate to Grade 4 of primary education, gender parity index (GPI)',
 'Survival rate to Grade 4 of primary education, male (%)',
 'Survival rate to Grade 5 of primary education, gender parity index (GPI)',
 'Survival rate to the last grade of primary education, gender parity index (GPI)',
 'Survival rate to the last grade of lower secondary general education, female (%)',
 'Survival rate to the last grade of lower secondary general education, male (%)',
 'Survival rate to the last grade of lower secondary general education, both sexes (%)',
 'Survival rate to the last grade of lower secondary general education, gender parity index (GPI)',
 'Teachers in lower secondary education, both sexes (number)',
 'Teachers in lower secondary education, female (number)',
 'Teachers in secondary general education, both sexes (number)',
 'Teachers in secondary general education, female (number)',
 'Teachers in secondary vocational education, both sexes (number)',
 'Teachers in secondary vocational education, female (number)',
 'Teachers in upper secondary education, both sexes (number)',
 'Teachers in upper secondary education, female (number)',
 'Teachers in post-secondary non-tertiary education, both sexes (number)',
 'Teachers in post-secondary non-tertiary education, female (number)',
 'Teachers in tertiary education ISCED 5 programmes, both sexes (number)',
 'Teachers in tertiary education ISCED 5 programmes, female (number)',
 'Enrolment in tertiary education per 100,000 inhabitants, both sexes',
 'Enrolment in tertiary education per 100,000 inhabitants, female',
 'Enrolment in tertiary education per 100,000 inhabitants, male',
 'Percentage of all students in tertiary education enrolled in ISCED 5, both sexes (%)',
 'Percentage of female students in tertiary education enrolled in ISCED 5',
 'Percentage of male students in tertiary education enrolled in ISCED 5',
 'Percentage of all students in tertiary education enrolled in ISCED 6, both sexes (%)',
 'Percentage of female students in tertiary education enrolled in ISCED 6',
 'Percentage of male students in tertiary education enrolled in ISCED 6',
 'Percentage of all students in tertiary education enrolled in ISCED 8, both sexes (%)',
 'Percentage of female students in tertiary education enrolled in ISCED 8',
 'Percentage of male students in tertiary education enrolled in ISCED 8',
 'Official entrance age to pre-primary education (years)',
 'Official entrance age to upper secondary education (years)',
 'Official entrance age to post-secondary non-tertiary education (years)',
 'Theoretical duration of pre-primary education (years)',
 'Theoretical duration of post-secondary non-tertiary education (years)',
 'Effective transition rate from primary to lower secondary general education, gender parity index (GPI)',
 'Percentage of teachers in pre-primary education who are trained, both sexes (%)',
 'Percentage of female teachers in pre-primary education who are trained, female (%)',
 'Percentage of teachers in pre-primary education who are trained, gender parity index (GPI)',
 'Percentage of male teachers in pre-primary education who are trained, male (%)',
 'Percentage of teachers in primary education who are trained, gender parity index (GPI)',
 'Percentage of teachers in lower secondary education who are trained, both sexes (%)',
 'Percentage of female teachers in lower secondary education who are trained, female (%)',
 'Percentage of teachers in lower secondary education who are trained, gender parity index (GPI)',
 'Percentage of male teachers in lower secondary education who are trained, male (%)',
 'Percentage of teachers in secondary education who are trained, gender parity index (GPI)',
 'Percentage of teachers in upper secondary education who are trained, both sexes (%)',
 'Percentage of female teachers in upper secondary education who are trained, female (%)',
 'Percentage of teachers in upper secondary education who are trained, gender parity index (GPI)',
 'Percentage of male teachers in upper secondary education who are trained, male (%)',
 'Percentage of students enrolled in primary education who are under-age, both sexes (%)',
 'Percentage of female students enrolled in primary education who are under-age, female (%)',
 'Percentage of male students enrolled in primary education who are under-age, male (%)',
 'Under-age enrolment ratio in primary education, both sexes (%)',
 'Under-age enrolment ratio in primary education, female (%)',
 'Under-age enrolment ratio in primary education, male (%)',
 'Under-age enrolment ratio in secondary education, both sexes (%)',
 'Under-age enrolment ratio in secondary education, female (%)',
 'Under-age enrolment ratio in secondary education, male (%)',
 'Government expenditure on pre-primary education as % of GDP (%)',
 'Government expenditure in pre-primary institutions as % of GDP (%)',
 'Government expenditure on primary education as % of GDP (%)',
 'Government expenditure in primary institutions as % of GDP (%)',
 'Government expenditure on secondary education as % of GDP (%)',
 'Government expenditure on post-secondary non-tertiary education as % of GDP (%)',
 'Government expenditure in post-secondary non-tertiary institutions as % of GDP (%)',
 'Government expenditure on tertiary education as % of GDP (%)',
 'Government expenditure in educational institutions as % of GDP (%)',
 'Expenditure on pre-primary as % of total government expenditure (%)',
 'Expenditure on primary as % of total government expenditure (%)',
 'Expenditure on lower secondary as % of total government expenditure (%)',
 'Expenditure on secondary as % of total government expenditure (%)',
 ...]
In [ ]:
df4_rows_list = df4['Indicator Name'].unique().tolist()
In [ ]:
df4_rows_list
Out[ ]:
['Internet users (per 100 people)',
 'GDP, PPP (current international $)',
 'GDP, PPP (constant 2011 international $)',
 'GDP per capita (current US$)',
 'GDP per capita, PPP (current international $)',
 'GDP per capita, PPP (constant 2011 international $)',
 'GNI (current US$)',
 'GNI, PPP (current international $)',
 'GNI per capita, Atlas method (current US$)',
 'GNI per capita, PPP (current international $)',
 'Annual statutory teacher salaries in public institutions in USD. Primary. Starting salary',
 'Annual statutory teacher salaries in public institutions in USD. Primary. 15 years of experience',
 'Annual statutory teacher salaries in public institutions in USD. Primary. Top of scale',
 'Annual statutory teacher salaries in public institutions in USD. Lower Secondary. Starting salary',
 'Annual statutory teacher salaries in public institutions in USD. Lower Secondary. 15 years of experience',
 'Annual statutory teacher salaries in public institutions in USD. Lower Secondary. Top of scale',
 'Annual statutory teacher salaries in public institutions in USD. Upper Secondary. Starting salary',
 'Annual statutory teacher salaries in public institutions in USD. Upper Secondary. 15 years of experience',
 'Annual statutory teacher salaries in public institutions in USD. Upper Secondary. Top of scale',
 'Youth literacy rate, population 15-24 years, female (%)',
 'Youth literacy rate, population 15-24 years, gender parity index (GPI)',
 'Youth literacy rate, population 15-24 years, male (%)',
 'Youth literacy rate, population 15-24 years, both sexes (%)',
 'Adult literacy rate, population 15+ years, female (%)',
 'Adult literacy rate, population 15+ years, male (%)',
 'Adult literacy rate, population 15+ years, both sexes (%)',
 'Duration of compulsory education (years)',
 'Gross enrolment ratio, primary, gender parity index (GPI)',
 'Gross enrolment ratio, primary and secondary, gender parity index (GPI)',
 'Gross enrolment ratio, secondary, gender parity index (GPI)',
 'Gross enrolment ratio, tertiary, gender parity index (GPI)',
 'Enrolment in pre-primary education, both sexes (number)',
 'Enrolment in pre-primary education, female (number)',
 'Percentage of students in pre-primary education who are female (%)',
 'Pupil-teacher ratio in pre-primary education (headcount basis)',
 'Gross enrolment ratio, pre-primary, both sexes (%)',
 'Gross enrolment ratio, pre-primary, female (%)',
 'Gross enrolment ratio, pre-primary, male (%)',
 'Net enrolment rate, pre-primary, both sexes (%)',
 'Net enrolment rate, pre-primary, female (%)',
 'Net enrolment rate, pre-primary, male (%)',
 'Percentage of enrolment in pre-primary education in private institutions (%)',
 'Teachers in pre-primary education, both sexes (number)',
 'Teachers in pre-primary education, female (number)',
 'Percentage of teachers in pre-primary education who are female (%)',
 'Official entrance age to primary education (years)',
 'Gross graduation ratio from primary education, female (%)',
 'Gross graduation ratio from primary education, male (%)',
 'Gross graduation ratio from primary education, both sexes (%)',
 'Primary completion rate, female (%)',
 'Primary completion rate, male (%)',
 'Primary completion rate, both sexes (%)',
 'Cumulative drop-out rate to the last grade of primary education, female (%)',
 'Cumulative drop-out rate to the last grade of primary education, male (%)',
 'Cumulative drop-out rate to the last grade of primary education, both sexes (%)',
 'Theoretical duration of primary education (years)',
 'Enrolment in primary education, both sexes (number)',
 'Enrolment in primary education, female (number)',
 'Percentage of students in primary education who are female (%)',
 'Pupil-teacher ratio in primary education (headcount basis)',
 'Gross enrolment ratio, primary, both sexes (%)',
 'Gross enrolment ratio, primary, female (%)',
 'Gross enrolment ratio, primary, male (%)',
 'Gross intake ratio to Grade 1 of primary education, female (%)',
 'Gross intake ratio to Grade 1 of primary education, male (%)',
 'Gross intake ratio to Grade 1 of primary education, both sexes (%)',
 'Net enrolment rate, primary, both sexes (%)',
 'Net enrolment rate, primary, female (%)',
 'Net enrolment rate, primary, male (%)',
 'Net intake rate to Grade 1 of primary education, female (%)',
 'Net intake rate to Grade 1 of primary education, male (%)',
 'Net intake rate to Grade 1 of primary education, both sexes (%)',
 'Percentage of enrolment in primary education in private institutions (%)',
 'Survival rate to Grade 5 of primary education, female (%)',
 'Survival rate to Grade 5 of primary education, male (%)',
 'Survival rate to Grade 5 of primary education, both sexes (%)',
 'Survival rate to the last grade of primary education, female (%)',
 'Survival rate to the last grade of primary education, male (%)',
 'Survival rate to the last grade of primary education, both sexes (%)',
 'Percentage of repeaters in primary education, all grades, female (%)',
 'Percentage of repeaters in primary education, all grades, male (%)',
 'Percentage of repeaters in primary education, all grades, both sexes (%)',
 'Percentage of female teachers in primary education who are trained, female (%)',
 'Percentage of male teachers in primary education who are trained, male (%)',
 'Percentage of teachers in primary education who are trained, both sexes (%)',
 'Teachers in primary education, both sexes (number)',
 'Teachers in primary education, female (number)',
 'Percentage of teachers in primary education who are female (%)',
 'Adjusted net enrolment rate, primary, both sexes (%)',
 'Adjusted net enrolment rate, primary, female (%)',
 'Adjusted net enrolment rate, primary, male (%)',
 'Out-of-school children of primary school age, both sexes (number)',
 'Out-of-school children of primary school age, female (number)',
 'Out-of-school children of primary school age, male (number)',
 'School life expectancy, primary to tertiary, both sexes (years)',
 'School life expectancy, primary to tertiary, female (years)',
 'School life expectancy, primary to tertiary, male (years)',
 'Official entrance age to lower secondary education (years)',
 'Lower secondary completion rate, female (%)',
 'Lower secondary completion rate, male (%)',
 'Lower secondary completion rate, both sexes (%)',
 'Theoretical duration of secondary education (years)',
 'Theoretical duration of lower secondary education (years)',
 'Theoretical duration of upper secondary education (years)',
 'Enrolment in secondary education, both sexes (number)',
 'Enrolment in secondary education, female (number)',
 'Percentage of female students in secondary education enrolled in vocational programmes, female (%)',
 'Percentage of students in secondary education who are female (%)',
 'Enrolment in secondary general, both sexes (number)',
 'Enrolment in secondary general, female (number)',
 'Percentage of students in secondary general education who are female (%)',
 'Percentage of male students in secondary education enrolled in vocational programmes, male (%)',
 'Pupil-teacher ratio in secondary education (headcount basis)',
 'Enrolment in secondary vocational, both sexes (number)',
 'Enrolment in secondary vocational, female (number)',
 'Percentage of students in secondary vocational education who are female (%)',
 'Percentage of students in secondary education enrolled in vocational programmes, both sexes (%)',
 'Gross enrolment ratio, secondary, both sexes (%)',
 'Gross enrolment ratio, secondary, female (%)',
 'Gross enrolment ratio, lower secondary, both sexes (%)',
 'Gross enrolment ratio, lower secondary, female (%)',
 'Gross enrolment ratio, lower secondary, male (%)',
 'Gross enrolment ratio, secondary, male (%)',
 'Gross enrolment ratio, upper secondary, both sexes (%)',
 'Gross enrolment ratio, upper secondary, female (%)',
 'Gross enrolment ratio, upper secondary, male (%)',
 'Net enrolment rate, secondary, both sexes (%)',
 'Net enrolment rate, secondary, female (%)',
 'Net enrolment rate, secondary, male (%)',
 'Percentage of enrolment in secondary education in private institutions (%)',
 'Effective transition rate from primary to lower secondary general education, female (%)',
 'Effective transition rate from primary to lower secondary general education, male (%)',
 'Effective transition rate from primary to lower secondary general education, both sexes (%)',
 'Percentage of female teachers in secondary education who are trained, female (%)',
 'Percentage of male teachers in secondary education who are trained, male (%)',
 'Percentage of teachers in secondary education who are trained, both sexes (%)',
 'Teachers in secondary education, both sexes (number)',
 'Teachers in secondary education, female (number)',
 'Percentage of teachers in secondary education who are female (%)',
 'Gross graduation ratio from first degree programmes (ISCED 6 and 7) in tertiary education, female (%)',
 'Gross graduation ratio from first degree programmes (ISCED 6 and 7) in tertiary education, male (%)',
 'Gross graduation ratio from first degree programmes (ISCED 6 and 7) in tertiary education, both sexes (%)',
 'Enrolment in tertiary education, all programmes, both sexes (number)',
 'Enrolment in tertiary education, all programmes, female (number)',
 'Percentage of students in tertiary education who are female (%)',
 'Gross enrolment ratio, tertiary, both sexes (%)',
 'Gross enrolment ratio, tertiary, female (%)',
 'Gross enrolment ratio, tertiary, male (%)',
 'Graduates from tertiary education, both sexes (number)',
 'Percentage of female graduates from tertiary education graduating from Agriculture programmes, female (%)',
 'Percentage of graduates from tertiary education graduating from Agriculture programmes, both sexes (%)',
 'Percentage of female graduates from tertiary education graduating from Education programmes, female (%)',
 'Percentage of graduates from tertiary education graduating from Education programmes, both sexes (%)',
 'Percentage of female graduates from tertiary education graduating from Engineering, Manufacturing and Construction programmes, female (%)',
 'Percentage of graduates from tertiary education graduating from Engineering, Manufacturing and Construction programmes, both sexes (%)',
 'Graduates from tertiary education, female (number)',
 'Percentage of graduates from Agriculture programmes in tertiary education who are female (%)',
 'Percentage of graduates from Education programmes in tertiary education who are female (%)',
 'Percentage of graduates from Engineering, Manufacturing and Construction programmes in tertiary education who are female (%)',
 'Percentage of graduates from Health and Welfare programmes in tertiary education who are female (%)',
 'Percentage of graduates from Humanities and Arts programmes in tertiary education who are female (%)',
 'Percentage of graduates from programmes in unspecified fields in tertiary education who are female (%)',
 'Percentage of graduates from Science programmes in tertiary education who are female (%)',
 'Percentage of graduates from Social Sciences, Business and Law programmes in tertiary education who are female (%)',
 'Percentage of graduates from Services programmes in tertiary education who are female (%)',
 'Percentage of graduates from tertiary education who are female (%)',
 'Percentage of female graduates from tertiary education graduating from Health and Welfare programmes, female (%)',
 'Percentage of graduates from tertiary education graduating from Health and Welfare programmes, both sexes (%)',
 'Percentage of female graduates from tertiary education graduating from Humanities and Arts programmes, female (%)',
 'Percentage of graduates from tertiary education graduating from Humanities and Arts programmes, both sexes (%)',
 'Percentage of female graduates from tertiary education graduating from programmes in unspecified fields, female (%)',
 'Percentage of graduates from tertiary education graduating from programmes in unspecified fields, both sexes (%)',
 'Percentage of female graduates from tertiary education graduating from Science programmes, female (%)',
 'Percentage of graduates from tertiary education graduating from Science programmes, both sexes (%)',
 'Percentage of female graduates from tertiary education graduating from Social Sciences, Business and Law programmes, female (%)',
 'Percentage of graduates from tertiary education graduating from Social Sciences, Business and Law programmes, both sexes (%)',
 'Percentage of female graduates from tertiary education graduating from Services programmes, female (%)',
 'Percentage of graduates from tertiary education graduating from Services programmes, both sexes (%)',
 'Percentage of enrolment in tertiary education in private institutions (%)',
 'Teachers in tertiary education programmes, both sexes (number)',
 'Teachers in tertiary education programmes, female (number)',
 'Percentage of teachers in tertiary education who are female (%)',
 'Gross enrolment ratio, primary to tertiary, both sexes (%)',
 'Current expenditure as % of total expenditure in public institutions (%)',
 'Government expenditure per primary student as % of GDP per capita (%)',
 'Expenditure on primary as % of government expenditure on education (%)',
 'Government expenditure per secondary student as % of GDP per capita (%)',
 'Expenditure on secondary as % of government expenditure on education (%)',
 'Government expenditure per tertiary student as % of GDP per capita (%)',
 'Expenditure on tertiary as % of government expenditure on education (%)',
 'Expenditure on education as % of total government expenditure (%)',
 'Government expenditure on education as % of GDP (%)',
 'Prevalence of HIV, total (% of population ages 15-49)',
 'Labor force, female (% of total labor force)',
 'Labor force, total',
 'Population, ages 3-5, female',
 'Population, ages 3-5, male',
 'Population, ages 3-5, total',
 'Population, ages 4-6, female',
 'Population, ages 4-6, male',
 'Population, ages 4-6, total',
 'Population, ages 5-9, female',
 'Population, ages 5-9, male',
 'Population, ages 5-9, total',
 'Population, ages 5-10, female',
 'Population, ages 5-10, male',
 'Population, ages 5-10, total',
 'Population, ages 5-11, female',
 'Population, ages 5-11, male',
 'Population, ages 5-11, total',
 'Population, ages 6-9, female',
 'Population, ages 6-9, male',
 'Population, ages 6-9, total',
 'Population, ages 6-10, female',
 'Population, ages 6-10, male',
 'Population, ages 6-10, total',
 'Population, ages 6-11, female',
 'Population, ages 6-11, male',
 'Population, ages 6-11, total',
 'Population, ages 6-12, female',
 'Population, ages 6-12, male',
 'Population, ages 6-12, total',
 'Population, ages 7-9, female',
 'Population, ages 7-9, male',
 'Population, ages 7-9, total',
 'Population, ages 7-10, female',
 'Population, ages 7-10, male',
 'Population, ages 7-10, total',
 'Population, ages 7-11, female',
 'Population, ages 7-11, male',
 'Population, ages 7-11, total',
 'Population, ages 7-12, female',
 'Population, ages 7-12, male',
 'Population, ages 7-12, total',
 'Population, ages 7-13, female',
 'Population, ages 7-13, male',
 'Population, ages 7-13, total',
 'Population, ages 10-14, female',
 'Population, ages 10-14, male',
 'Population, ages 10-14, total',
 'Population, ages 10-15, female',
 'Population, ages 10-15, male',
 'Population, ages 10-15, total',
 'Population, ages 10-16, female',
 'Population, ages 10-16, male',
 'Population, ages 10-16, total',
 'Population, ages 10-17, female',
 'Population, ages 10-17, male',
 'Population, ages 10-17, total',
 'Population, ages 10-18, female',
 'Population, ages 10-18, male',
 'Population, ages 10-18, total',
 'Population, ages 11-15, female',
 'Population, ages 11-15, male',
 'Population, ages 11-15, total',
 'Population, ages 11-16, female',
 'Population, ages 11-16, male',
 'Population, ages 11-16, total',
 'Population, ages 11-17, female',
 'Population, ages 11-17, male',
 'Population, ages 11-17, total',
 'Population, ages 11-18, female',
 'Population, ages 11-18, male',
 'Population, ages 11-18, total',
 'Population, ages 12-15, female',
 'Population, ages 12-15, male',
 'Population, ages 12-15, total',
 'Population, ages 12-16, female',
 'Population, ages 12-16, male',
 'Population, ages 12-16, total',
 'Population, ages 12-17, female',
 'Population, ages 12-17, male',
 'Population, ages 12-17, total',
 'Population, ages 12-18, female',
 'Population, ages 12-18, male',
 'Population, ages 12-18, total',
 'Population, ages 13-16, female',
 'Population, ages 13-16, male',
 'Population, ages 13-16, total',
 'Population, ages 13-17, female',
 'Population, ages 13-17, male',
 'Population, ages 13-17, total',
 'Population, ages 13-18, female',
 'Population, ages 13-18, male',
 'Population, ages 13-18, total',
 'Population, ages 13-19, female',
 'Population, ages 13-19, male',
 'Population, ages 13-19, total',
 'Population, ages 14-18, female',
 'Population, ages 14-18, male',
 'Population, ages 14-18, total',
 'Population, ages 14-19, female',
 'Population, ages 14-19, male',
 'Population, ages 14-19, total',
 'Population, ages 15-24, female',
 'Population, ages 15-24, male',
 'Population, ages 15-24, total',
 'Population, age 0, male',
 'Population, age 1, male',
 'Population, age 2, male',
 'Population, age 3, male',
 'Population, age 4, male',
 'Population, age 5, male',
 'Population, age 6, male',
 'Population, age 7, male',
 'Population, age 8, male',
 'Population, age 9, male',
 'Population, age 10, male',
 'Population, age 11, male',
 'Population, age 12, male',
 'Population, age 13, male',
 'Population, age 14, male',
 'Population, age 15, male',
 'Population, age 16, male',
 'Population, age 17, male',
 'Population, age 18, male',
 'Population, age 19, male',
 'Population, age 20, male',
 'Population, age 21, male',
 'Population, age 22, male',
 'Population, age 23, male',
 'Population, age 24, male',
 'Population, age 25, male',
 'Population growth (annual %)',
 'Population, total',
 'Population, female',
 'Population, female (% of total)',
 'Population, male',
 'Population, male (% of total)',
 'Population of the official age for pre-primary education, female (number)',
 'Population of the official age for pre-primary education, both sexes (number)',
 'Population of the official age for pre-primary education, male (number)',
 'Population of the official age for the last grade of primary education, female (number)',
 'Population of the official age for the last grade of primary education, male (number)',
 'Population of the official age for the last grade of primary education, both sexes (number)',
 'Population of the official age for primary education, female (number)',
 'Population of the official age for primary education, both sexes (number)',
 'Population of the official age for primary education, male (number)',
 'Population of the official age for lower secondary education, female (number)',
 'Population of the official age for lower secondary education, both sexes (number)',
 'Population of the official age for lower secondary education, male (number)',
 'Population of the official age for secondary education, female (number)',
 'Population of the official age for secondary education, both sexes (number)',
 'Population of the official age for secondary education, male (number)',
 'Population of the official age for upper secondary education, female (number)',
 'Population of the official age for upper secondary education, both sexes (number)',
 'Population of the official age for upper secondary education, male (number)',
 'Population of the official age for tertiary education, female (number)',
 'Population of the official age for tertiary education, both sexes (number)',
 'Population of the official age for tertiary education, male (number)',
 'Primary completion rate, gender parity index (GPI)',
 'Gross intake ratio to Grade 1 of primary education, gender parity index (GPI)',
 'Gross intake ratio to Grade 1 of lower secondary general education, both sexes (%)',
 'Gross intake ratio to Grade 1 of lower secondary general education, female (%)',
 'Lower secondary completion rate, gender parity index (GPI)',
 'Gross intake ratio to Grade 1 of lower secondary general education, gender parity index (GPI)',
 'Gross intake ratio to Grade 1 of lower secondary general education, male (%)',
 'Official entrance age to compulsory education (years)',
 'Drop-out rate from Grade 1 of primary education, both sexes (%)',
 'Drop-out rate from Grade 1 of primary education, female (%)',
 'Drop-out rate from Grade 1 of primary education, male (%)',
 'Drop-out rate from Grade 2 of primary education, both sexes (%)',
 'Drop-out rate from Grade 2 of primary education, female (%)',
 'Drop-out rate from Grade 2 of primary education, male (%)',
 'Drop-out rate from Grade 3 of primary education, both sexes (%)',
 'Drop-out rate from Grade 3 of primary education, female (%)',
 'Drop-out rate from Grade 3 of primary education, male (%)',
 'Drop-out rate from Grade 4 of primary education, both sexes (%)',
 'Drop-out rate from Grade 4 of primary education, female (%)',
 'Drop-out rate from Grade 4 of primary education, male (%)',
 'Drop-out rate from Grade 5 of primary education, both sexes (%)',
 'Drop-out rate from Grade 5 of primary education, female (%)',
 'Drop-out rate from Grade 5 of primary education, male (%)',
 'Drop-out rate from Grade 6 of primary education, both sexes (%)',
 'Drop-out rate from Grade 6 of primary education, female (%)',
 'Drop-out rate from Grade 6 of primary education, male (%)',
 'Cumulative drop-out rate to the last grade of lower secondary general education, female (%)',
 'Drop-out rate from Grade 1 of lower secondary general education, female (%)',
 'Drop-out rate from Grade 1 of lower secondary general education, male (%)',
 'Drop-out rate from Grade 1 of lower secondary general education, both sexes (%)',
 'Drop-out rate from Grade 2 of lower secondary general education, female (%)',
 'Drop-out rate from Grade 2 of lower secondary general education, male (%)',
 'Drop-out rate from Grade 2 of lower secondary general education, both sexes (%)',
 'Drop-out rate from Grade 3 of lower secondary general education, female (%)',
 'Drop-out rate from Grade 3 of lower secondary general education, male (%)',
 'Drop-out rate from Grade 3 of lower secondary general education, both sexes (%)',
 'Drop-out rate from Grade 4 of lower secondary general education, female (%)',
 'Drop-out rate from Grade 4 of lower secondary general education, male (%)',
 'Drop-out rate from Grade 4 of lower secondary general education, both sexes (%)',
 'Cumulative drop-out rate to the last grade of lower secondary general education, male (%)',
 'Cumulative drop-out rate to the last grade of lower secondary general education, both sexes (%)',
 'Enrolment in early childhood education, female (number)',
 'Enrolment in early childhood education, private institutions, female (number)',
 'Enrolment in early childhood education, private institutions, both sexes (number)',
 'Enrolment in early childhood education, public institutions, female (number)',
 'Enrolment in early childhood education, public institutions, both sexes (number)',
 'Enrolment in early childhood education, both sexes (number)',
 'Enrolment in early childhood educational development programmes, female (number)',
 'Enrolment in early childhood educational development programmes, private institutions, female (number)',
 'Enrolment in early childhood educational development programmes, private institutions, both sexes (number)',
 'Enrolment in early childhood educational development programmes, public institutions, female (number)',
 'Enrolment in early childhood educational development programmes, public institutions, both sexes (number)',
 'Enrolment in early childhood educational development programmes, both sexes (number)',
 'Enrolment in pre-primary education, private institutions, both sexes (number)',
 'Enrolment in pre-primary education, private institutions, female (number)',
 'Enrolment in pre-primary education, public institutions, both sexes (number)',
 'Enrolment in pre-primary education, public institutions, female (number)',
 'Enrolment in Grade 1 of primary education, both sexes (number)',
 'Enrolment in Grade 1 of primary education, female (number)',
 'Enrolment in Grade 2 of primary education, both sexes (number)',
 'Enrolment in Grade 2 of primary education, female (number)',
 'Enrolment in Grade 3 of primary education, both sexes (number)',
 'Enrolment in Grade 3 of primary education, female (number)',
 'Enrolment in Grade 4 of primary education, both sexes (number)',
 'Enrolment in Grade 4 of primary education, female (number)',
 'Enrolment in Grade 5 of primary education, both sexes (number)',
 'Enrolment in Grade 5 of primary education, female (number)',
 'Enrolment in Grade 6 of primary education, both sexes (number)',
 'Enrolment in Grade 6 of primary education, female (number)',
 'Enrolment in Grade 7 of primary education, both sexes (number)',
 'Enrolment in Grade 7 of primary education, female (number)',
 'Enrolment in primary education, Grade unspecified, both sexes (number)',
 'Enrolment in primary education, Grade unspecified, female (number)',
 'Enrolment in primary education, private institutions, both sexes (number)',
 'Enrolment in primary education, private institutions, female (number)',
 'Enrolment in primary education, public institutions, both sexes (number)',
 'Enrolment in primary education, public institutions, female (number)',
 'Enrolment in lower secondary education, both sexes (number)',
 'Enrolment in lower secondary education, female (number)',
 'Enrolment in lower secondary general, both sexes (number)',
 'Enrolment in lower secondary general, female (number)',
 'Enrolment in Grade 1 of lower secondary general education, female (number)',
 'Enrolment in Grade 1 of lower secondary general education, both sexes (number)',
 'Enrolment in Grade 2 of lower secondary general education, female (number)',
 'Enrolment in Grade 2 of lower secondary general education, both sexes (number)',
 'Enrolment in Grade 3 of lower secondary general education, female (number)',
 'Enrolment in Grade 3 of lower secondary general education, both sexes (number)',
 'Enrolment in Grade 4 of lower secondary general education, female (number)',
 'Enrolment in Grade 4 of lower secondary general education, both sexes (number)',
 'Enrolment in Grade 5 of lower secondary general education, female (number)',
 'Enrolment in Grade 5 of lower secondary general education, both sexes (number)',
 'Enrolment in lower secondary general education, Grade unspecified, female (number)',
 'Enrolment in lower secondary general education, Grade unspecified, both sexes (number)',
 'Enrolment in lower secondary education, private institutions, both sexes (number)',
 'Enrolment in lower secondary education, private institutions, female (number)',
 'Enrolment in lower secondary education, public institutions, both sexes (number)',
 'Enrolment in lower secondary education, public institutions, female (number)',
 'Enrolment in lower secondary vocational, both sexes (number)',
 'Enrolment in lower secondary vocational, female (number)',
 'Enrolment in secondary education, private institutions, both sexes (number)',
 'Enrolment in secondary education, private institutions, female (number)',
 'Enrolment in secondary education, public institutions, both sexes (number)',
 'Enrolment in secondary education, public institutions, female (number)',
 'Enrolment in upper secondary education, both sexes (number)',
 'Enrolment in upper secondary education, female (number)',
 'Enrolment in upper secondary general, both sexes (number)',
 'Enrolment in upper secondary general, female (number)',
 'Enrolment in upper secondary education, private institutions, both sexes (number)',
 'Enrolment in upper secondary education, private institutions, female (number)',
 'Enrolment in upper secondary education, public institutions, both sexes (number)',
 'Enrolment in upper secondary education, public institutions, female (number)',
 'Enrolment in upper secondary vocational, both sexes (number)',
 'Enrolment in upper secondary vocational, female (number)',
 'Enrolment in post-secondary non-tertiary education, both sexes (number)',
 'Enrolment in post-secondary non-tertiary education, female (number)',
 'Enrolment in post-secondary non-tertiary education, private institutions, both sexes (number)',
 'Enrolment in post-secondary non-tertiary education, private institutions, female (number)',
 'Enrolment in post-secondary non-tertiary education, public institutions, both sexes (number)',
 'Enrolment in post-secondary non-tertiary education, public institutions, female (number)',
 'Enrolment in tertiary education, ISCED 5 programmes, both sexes (number)',
 'Enrolment in tertiary education, ISCED 5 programmes, female (number)',
 'Enrolment in tertiary education, ISCED 6 programmes, both sexes (number)',
 'Enrolment in tertiary education, ISCED 6 programmes, female (number)',
 'Enrolment in tertiary education, ISCED 8 programmes, both sexes (number)',
 'Enrolment in tertiary education, ISCED 8 programmes, female (number)',
 'UIS: Percentage of population age 25+ with completed primary education. Total',
 'UIS: Percentage of population age 25+ with completed primary education. Female',
 'UIS: Percentage of population age 25+ with completed primary education. Male',
 'UIS: Percentage of population age 25+ with at least completed primary education (ISCED 1 or higher). Total',
 'UIS: Percentage of population age 25+ with at least completed primary education (ISCED 1 or higher). Female',
 'UIS: Percentage of population age 25+ with at least completed primary education (ISCED 1 or higher). Gender Parity Index',
 'UIS: Percentage of population age 25+ with at least completed primary education (ISCED 1 or higher). Male',
 'UIS: Percentage of population age 25+ with completed lower secondary education. Total',
 'UIS: Percentage of population age 25+ with completed lower secondary education. Female',
 'UIS: Percentage of population age 25+ with completed lower secondary education. Male',
 'UIS: Percentage of population age 25+ with at least completed lower secondary education (ISCED 2 or higher). Total',
 'UIS: Percentage of population age 25+ with at least completed lower secondary education (ISCED 2 or higher). Female',
 'UIS: Percentage of population age 25+ with at least completed lower secondary education (ISCED 2 or higher). Gender Parity Index',
 'UIS: Percentage of population age 25+ with at least completed lower secondary education (ISCED 2 or higher). Male',
 'UIS: Percentage of population age 25+ with completed upper secondary education. Total',
 'UIS: Percentage of population age 25+ with completed upper secondary education. Female',
 'UIS: Percentage of population age 25+ with completed upper secondary education. Male',
 'UIS: Percentage of population age 25+ with at least completed upper secondary education (ISCED 3 or higher). Total',
 'UIS: Percentage of population age 25+ with at least completed upper secondary education (ISCED 3 or higher). Female',
 'UIS: Percentage of population age 25+ with at least completed upper secondary education (ISCED 3 or higher). Gender Parity Index',
 'UIS: Percentage of population age 25+ with at least completed upper secondary education (ISCED 3 or higher). Male',
 'UIS: Percentage of population age 25+ with completed post-secondary education. Total',
 'UIS: Percentage of population age 25+ with completed post-secondary education. Female',
 'UIS: Percentage of population age 25+ with completed post-secondary education. Male',
 'UIS: Percentage of population age 25+ with at least completed post-secondary education (ISCED 4 or higher). Total',
 'UIS: Percentage of population age 25+ with at least completed post-secondary education (ISCED 4 or higher). Female',
 'UIS: Percentage of population age 25+ with at least completed post-secondary education (ISCED 4 or higher). Gender Parity Index',
 'UIS: Percentage of population age 25+ with at least completed post-secondary education (ISCED 4 or higher). Male',
 'UIS: Percentage of population age 25+ with at least a completed short-cycle tertiary degree (ISCED 5 or higher). Total',
 'UIS: Percentage of population age 25+ with at least a completed short-cycle tertiary degree (ISCED 5 or higher). Female',
 'UIS: Percentage of population age 25+ with at least a completed short-cycle tertiary degree (ISCED 5 or higher). Gender Parity Index',
 'UIS: Percentage of population age 25+ with at least a completed short-cycle tertiary degree (ISCED 5 or higher). Male',
 "UIS: Percentage of population age 25+ with a completed bachelor's or equivalent degree (ISCED 6). Total",
 "UIS: Percentage of population age 25+ with a completed bachelor's or equivalent degree (ISCED 6). Female",
 "UIS: Percentage of population age 25+ with a completed bachelor's or equivalent degree (ISCED 6). Male",
 'UIS: Mean years of schooling of the population age 25+. Total',
 'UIS: Mean years of schooling of the population age 25+. Female',
 'UIS: Mean years of schooling of the population age 25+. Male',
 'UIS: Percentage of population age 25+ with no schooling. Total',
 'UIS: Percentage of population age 25+ with no schooling. Female',
 'UIS: Percentage of population age 25+ with no schooling. Male',
 'UIS: Percentage of population age 25+ with some primary education. Total',
 'UIS: Percentage of population age 25+ with some primary education. Female',
 'UIS: Percentage of population age 25+ with some primary education. Male',
 'UIS: Percentage of population age 25+ with unknown educational attainment. Total',
 'UIS: Percentage of population age 25+ with unknown educational attainment. Female',
 'UIS: Percentage of population age 25+ with unknown educational attainment. Male',
 'Percentage of new entrants to Grade 1 of primary education with early childhood educational experience, both sexes (%)',
 'Percentage of new entrants to Grade 1 of primary education with early childhood educational experience, female (%)',
 'Percentage of new entrants to Grade 1 of primary education with early childhood educational experience, gender parity index (GPI)',
 'Percentage of new entrants to Grade 1 of primary education with early childhood educational experience, male (%)',
 'Early school leavers from primary education, female (number)',
 'Early school leavers from primary education, male (number)',
 'Early school leavers from primary education, both sexes (number)',
 'Percentage of students in early childhood education who are female (%)',
 'Percentage of students in early childhood educational development programmes who are female (%)',
 'Percentage of students in lower secondary education who are female (%)',
 'Percentage of students in lower secondary general education who are female (%)',
 'Percentage of students in lower secondary vocational education who are female (%)',
 'Percentage of students in upper secondary education who are female (%)',
 'Percentage of students in upper secondary general education who are female (%)',
 'Percentage of students in upper secondary vocational education who are female (%)',
 'Percentage of students in post-secondary non-tertiary education who are female (%)',
 'Percentage of students in post-secondary non-tertiary general education who are female (%)',
 'Percentage of students in post-secondary non-tertiary vocational education who are female (%)',
 'Percentage of students in tertiary ISCED 5 programmes who are female (%)',
 'Percentage of students enrolled in Education programmes in tertiary education who are female (%)',
 'Percentage of students enrolled in Humanities and Arts programmes in tertiary education who are female (%)',
 'Percentage of students enrolled in Social Sciences, Business and Law programmes in tertiary education who are female (%)',
 'Percentage of students enrolled in Science programmes in tertiary education who are female (%)',
 'Percentage of students enrolled in Engineering, Manufacturing and Construction programmes in tertiary education who are female (%)',
 'Percentage of students enrolled in Agriculture programmes in tertiary education who are female (%)',
 'Percentage of students enrolled in Health and Welfare programmes in tertiary education who are female (%)',
 'Percentage of students enrolled in Services programmes in tertiary education who are female (%)',
 'Percentage of all inbound internationally mobile students in tertiary education in the host country who are female, (%)',
 'Percentage of students enrolled in programmes in unspecified fields in tertiary education who are female (%)',
 'Percentage of students in tertiary ISCED 6 programmes who are female (%)',
 'Percentage of students in tertiary ISCED 8 programmes who are female (%)',
 'Percentage of graduates from tertiary ISCED 5 programmes who are female (%)',
 'Percentage of graduates from Science and Technology programmes in tertiary education who are female (%)',
 'Percentage of graduates from non-science and non-technology fields in tertiary education who are female (%)',
 'Percentage of graduates from tertiary ISCED 6 programmes who are female (%)',
 'Percentage of graduates from tertiary ISCED 8 programmes who are female (%)',
 'New entrants to Grade 1 of primary education, percentage female (%)',
 'Out-of-school children of primary school age, percentage female (%)',
 'Out-of-school adolescents of lower secondary school age, percentage female (%)',
 'Out-of-school youth of upper secondary school age, percentage female (%)',
 'Percentage of students in tertiary education enrolled in Education programmes, both sexes (%)',
 'Percentage of female students in tertiary education enrolled in Education programmes, female (%)',
 'Percentage of male students in tertiary education enrolled in Education programmes, male (%)',
 'Percentage of students in tertiary education enrolled in Humanities and Arts programmes, both sexes (%)',
 'Percentage of female students in tertiary education enrolled in Humanities and Arts programmes, female (%)',
 'Percentage of male students in tertiary education enrolled in Humanities and Arts programmes, male (%)',
 'Percentage of students in tertiary education enrolled in Social Sciences, Business and Law programmes, both sexes (%)',
 'Percentage of female students in tertiary education enrolled in Social Sciences, Business and Law programmes, female (%)',
 'Percentage of male students in tertiary education enrolled in Social Sciences, Business and Law programmes, male (%)',
 'Percentage of students in tertiary education enrolled in Science programmes, both sexes (%)',
 'Percentage of female students in tertiary education enrolled in Science programmes, female (%)',
 'Percentage of male students in tertiary education enrolled in Science programmes, male (%)',
 'Percentage of students in tertiary education enrolled in Engineering, Manufacturing and Construction programmes, both sexes (%)',
 'Percentage of female students in tertiary education enrolled in Engineering, Manufacturing and Construction programmes, female (%)',
 'Percentage of male students in tertiary education enrolled in Engineering, Manufacturing and Construction programmes, male (%)',
 'Percentage of students in tertiary education enrolled in Agriculture programmes, both sexes (%)',
 'Percentage of female students in tertiary education enrolled in Agriculture programmes, female (%)',
 'Percentage of male students in tertiary education enrolled in Agriculture programmes, male (%)',
 'Percentage of students in tertiary education enrolled in Health and Welfare programmes, both sexes (%)',
 'Percentage of female students in tertiary education enrolled in Health and Welfare programmes, female (%)',
 'Percentage of male students in tertiary education enrolled in Health and Welfare programmes, male (%)',
 'Percentage of students in tertiary education enrolled in Services programmes, both sexes (%)',
 'Percentage of female students in tertiary education enrolled in Services programmes, female (%)',
 'Percentage of male students in tertiary education enrolled in Services programmes, male (%)',
 'Percentage of students in tertiary education enrolled in programmes in unspecified fields, both sexes (%)',
 'Percentage of female students in tertiary education enrolled in programmes in unspecified fields, female (%)',
 'Percentage of male students in tertiary education enrolled in programmes in unspecified fields, male (%)',
 'Percentage of male graduates from tertiary education graduating from Education programmes, male (%)',
 'Percentage of male graduates from tertiary education graduating from Humanities and Arts programmes, male (%)',
 'Percentage of male graduates from tertiary education graduating from Social Sciences, Business and Law programmes, male (%)',
 'Percentage of male graduates from tertiary education graduating from Science programmes, male (%)',
 'Percentage of male graduates from tertiary education graduating from Engineering, Manufacturing and Construction programmes, male (%)',
 'Percentage of male graduates from tertiary education graduating from Agriculture programmes, male (%)',
 'Percentage of male graduates from tertiary education graduating from Health and Welfare programmes, male (%)',
 'Percentage of male graduates from tertiary education graduating from Services programmes, male (%)',
 'Percentage of male graduates from tertiary education graduating from programmes in unspecified fields, male (%)',
 'Percentage of teachers in lower secondary education who are female (%)',
 'Percentage of teachers in upper secondary education who are female (%)',
 'Percentage of teachers in post-secondary non-tertiary education who are female (%)',
 'Graduates from ISCED 5 programmes in tertiary education, female (number)',
 'Graduates from ISCED 5 programmes in tertiary education, both sexes (number)',
 'Graduates from ISCED 6 programmes in tertiary education, female (number)',
 'Graduates from ISCED 6 programmes in tertiary education, both sexes (number)',
 'Graduates from ISCED 8 programmes in tertiary education, female (number)',
 'Graduates from ISCED 8 programmes in tertiary education, both sexes (number)',
 'Gross enrolment ratio, pre-primary, gender parity index (GPI)',
 'Gross enrolment ratio, primary and lower secondary, both sexes (%)',
 'Gross enrolment ratio, primary and lower secondary, female (%)',
 'Gross enrolment ratio, primary and lower secondary, gender parity index (GPI)',
 'Gross enrolment ratio, primary and lower secondary, male (%)',
 'Gross enrolment ratio, primary and secondary, both sexes (%)',
 'Gross enrolment ratio, primary and secondary, female (%)',
 'Gross enrolment ratio, primary and secondary, male (%)',
 'Gross enrolment ratio, primary to tertiary, female (%)',
 'Gross enrolment ratio, primary to tertiary, gender parity index (GPI)',
 'Gross enrolment ratio, primary to tertiary, male (%)',
 'Gross enrolment ratio, lower secondary, gender parity index (GPI)',
 'Gross enrolment ratio, upper secondary, gender parity index (GPI)',
 'Gross enrolment ratio, post-secondary non-tertiary, both sexes (%)',
 'Gross enrolment ratio, post-secondary non-tertiary, female (%)',
 'Gross enrolment ratio, post-secondary non-tertiary, gender parity index (GPI)',
 'Gross enrolment ratio, post-secondary non-tertiary, male (%)',
 'Gross graduation ratio from primary education, gender parity index (GPI)',
 'Gross graduation ratio from lower secondary education, both sexes (%)',
 'Gross graduation ratio from lower secondary education, female (%)',
 'Gross graduation ratio from lower secondary education, gender parity index (GPI)',
 'Gross graduation ratio from lower secondary education, male (%)',
 'Gross graduation ratio from first degree programmes (ISCED 6 and 7) in tertiary education, gender parity index (GPI)',
 'Gross outbound enrolment ratio, all regions, both sexes (%)',
 'Percentage of students in lower secondary education enrolled in general programmes, both sexes (%)',
 'Percentage of female students in lower secondary education enrolled in general programmes, female (%)',
 'Percentage of male students in lower secondary education enrolled in general programmes, male (%)',
 'Percentage of students in lower secondary education enrolled in vocational programmes, both sexes (%)',
 'Percentage of female students in lower secondary education enrolled in vocational programmes, female (%)',
 'Percentage of male students in lower secondary education enrolled in vocational programmes, male (%)',
 'Percentage of students in secondary education enrolled in general programmes, both sexes (%)',
 'Percentage of female students in secondary education enrolled in general programmes, female (%)',
 'Percentage of male students in secondary education enrolled in general programmes, male (%)',
 'Percentage of students in upper secondary education enrolled in general programmes, both sexes (%)',
 'Percentage of female students in upper secondary education enrolled in general programmes, female (%)',
 'Percentage of male students in upper secondary education enrolled in general programmes, male (%)',
 'Percentage of students in upper secondary education enrolled in vocational programmes, both sexes (%)',
 'Percentage of female students in upper secondary education enrolled in vocational programmes, female (%)',
 'Percentage of male students in upper secondary education enrolled in vocational programmes, male (%)',
 'Percentage of students in post-secondary non-tertiary education enrolled in general programmes, both sexes (%)',
 'Percentage of female students in post-secondary non-tertiary education enrolled in general programmes, female (%)',
 'Percentage of male students in post-secondary non-tertiary education enrolled in general programmes, male (%)',
 'Percentage of students in post-secondary non-tertiary education enrolled in vocational programmes, both sexes (%)',
 'Percentage of female students in post-secondary non-tertiary education enrolled in vocational programmes, female (%)',
 'Percentage of male students in post-secondary non-tertiary education enrolled in vocational programmes, male (%)',
 'Illiterate population, 25-64 years, both sexes (number)',
 'Illiterate population, 25-64 years, female (number)',
 'Illiterate population, 25-64 years, male (number)',
 'Illiterate population, 25-64 years, % female',
 'Youth illiterate population, 15-24 years, both sexes (number)',
 'Youth illiterate population, 15-24 years, female (number)',
 'Youth illiterate population, 15-24 years, male (number)',
 'Adult illiterate population, 15+ years, both sexes (number)',
 'Adult illiterate population, 15+ years, female (number)',
 'Adult illiterate population, 15+ years, male (number)',
 'Elderly illiterate population, 65+ years, both sexes (number)',
 'Elderly illiterate population, 65+ years, female (number)',
 'Elderly illiterate population, 65+ years, male (number)',
 'Youth illiterate population, 15-24 years, % female',
 'Adult illiterate population, 15+ years, % female',
 'Elderly illiterate population, 65+ years, % female',
 'Adult literacy rate, population 15+ years, gender parity index (GPI)',
 'Literacy rate, population 25-64 years, both sexes (%)',
 'Literacy rate, population 25-64 years, female (%)',
 'Literacy rate, population 25-64 years, gender parity index (GPI)',
 'Literacy rate, population 25-64 years, male (%)',
 'Elderly literacy rate, population 65+ years, both sexes (%)',
 'Elderly literacy rate, population 65+ years, female (%)',
 'Elderly literacy rate, population 65+ years, gender parity index (GPI)',
 'Elderly literacy rate, population 65+ years, male (%)',
 'Net flow of internationally mobile students (inbound - outbound), both sexes (number)',
 'Net flow ratio of internationally mobile students (inbound - outbound), both sexes (%)',
 'Total inbound internationally mobile students, female (number)',
 'Total inbound internationally mobile students, both sexes (number)',
 'Inbound mobility rate, both sexes (%)',
 'Inbound mobility rate, female (%)',
 'Inbound mobility rate, male (%)',
 'New entrants to Grade 1 of primary education with early childhood education experience , both sexes (number)',
 'New entrants to Grade 1 of primary education with early childhood education experience, female (number)',
 'New entrants to Grade 1 of primary education, both sexes (number)',
 'New entrants to Grade 1 of primary education, female (number)',
 'Net enrolment rate, pre-primary, gender parity index (GPI)',
 'Net enrolment rate, primary, gender parity index (GPI)',
 'Net enrolment rate, lower secondary, both sexes (%)',
 'Net enrolment rate, lower secondary, female (%)',
 'Net enrolment rate, lower secondary, male (%)',
 'Net enrolment rate, secondary, gender parity index (GPI)',
 'Net enrolment rate, upper secondary, both sexes (%)',
 'Net enrolment rate, upper secondary, female (%)',
 'Net enrolment rate, upper secondary, male (%)',
 'Adjusted net enrolment rate, primary, gender parity index (GPI)',
 'Adjusted net enrolment rate, lower secondary, both sexes (%)',
 'Adjusted net enrolment rate, lower secondary, female (%)',
 'Adjusted net enrolment rate, lower secondary, gender parity index (GPI)',
 'Adjusted net enrolment rate, lower secondary, male (%)',
 'Adjusted net enrolment rate, upper secondary, both sexes (%)',
 'Adjusted net enrolment rate, upper secondary, female (%)',
 'Adjusted net enrolment rate, upper secondary, gender parity index (GPI)',
 'Adjusted net enrolment rate, upper secondary, male (%)',
 'Total net enrolment rate, primary, both sexes (%)',
 'Total net enrolment rate, primary, female (%)',
 'Total net enrolment rate, primary, gender parity index (GPI)',
 'Total net enrolment rate, primary, male (%)',
 'Total net enrolment rate, lower secondary, both sexes (%)',
 'Total net enrolment rate, lower secondary, female (%)',
 'Total net enrolment rate, lower secondary, gender parity index (GPI)',
 'Total net enrolment rate, lower secondary, male (%)',
 'Net intake rate to Grade 1 of primary education by under-age entrants (-1 year), both sexes (%)',
 'Net intake rate to Grade 1 of primary education by under-age entrants (-1 year), female (%)',
 'Net intake rate to Grade 1 of primary education by under-age entrants (-1 year), gender parity index (GPI)',
 'Net intake rate to Grade 1 of primary education by under-age entrants (-1 year), male (%)',
 'Net intake rate to Grade 1 of primary education by over-age entrants (+1 year), both sexes (%)',
 'Net intake rate to Grade 1 of primary education by over-age entrants (+1 year), female (%)',
 'Net intake rate to Grade 1 of primary education by over-age entrants (+1 year), gender parity index (GPI)',
 'Net intake rate to Grade 1 of primary education by over-age entrants (+1 year), male (%)',
 'Net intake rate to Grade 1 of primary education, gender parity index (GPI)',
 'Adjusted net intake rate to Grade 1 of primary education, both sexes (%)',
 'Adjusted net intake rate to Grade 1 of primary education, female (%)',
 'Adjusted net intake rate to Grade 1 of primary education, gender parity index (GPI)',
 'Adjusted net intake rate to Grade 1 of primary education, male (%)',
 'Percentage of students enrolled in primary education who are over-age, both sexes (%)',
 'Percentage of female students enrolled in primary education who are over-age, female (%)',
 'Percentage of male students enrolled in primary education who are over-age, male (%)',
 'Over-age enrolment ratio in primary education, both sexes (%)',
 'Over-age enrolment ratio in primary education, female (%)',
 'Over-age enrolment ratio in primary education, male (%)',
 'Total outbound internationally mobile tertiary students studying abroad, all countries, both sexes (number)',
 'Out-of-school children of primary school age in pre-primary education, both sexes (number)',
 'Out-of-school children of primary school age in pre-primary education, female (number)',
 'Out-of-school children of primary school age in pre-primary education, male (number)',
 'Out-of-school adolescents of lower secondary school age, both sexes (number)',
 'Out-of-school adolescents of lower secondary school age, female (number)',
 'Out-of-school adolescents of lower secondary school age, male (number)',
 'Out-of-school youth of upper secondary school age, both sexes (number)',
 'Out-of-school youth of upper secondary school age, female (number)',
 'Out-of-school youth of upper secondary school age, male (number)',
 'Outbound mobility ratio, all regions, both sexes (%)',
 'Percentage of enrolment in early childhood education programmes in private institutions (%)',
 'Percentage of enrolment in early childhood educational development programmes in private institutions (%)',
 'Percentage of enrolment in lower secondary education in private institutions (%)',
 'Percentage of enrolment in upper secondary education in private institutions (%)',
 'Percentage of enrolment in post-secondary non-tertiary education in private institutions (%)',
 'Pupil/trained teacher ratio in pre-primary education (headcount basis)',
 'Pupil/trained teacher ratio in primary education (headcount basis)',
 'Pupil-teacher ratio in lower secondary education (headcount basis)',
 'Pupil/trained teacher ratio in lower secondary education (headcount basis)',
 'Pupil/trained teacher ratio in secondary education (headcount basis)',
 'Pupil-teacher ratio in upper secondary education (headcount basis)',
 'Pupil/trained teacher ratio in upper secondary education (headcount basis)',
 'Pupil-teacher ratio in tertiary education (headcount basis)',
 'Repeaters in primary education, all grades, both sexes (number)',
 'Repeaters in primary education, all grades, female (number)',
 'Repeaters in Grade 1 of primary education, both sexes (number)',
 'Repeaters in Grade 1 of primary education, female (number)',
 'Repeaters in Grade 2 of primary education, both sexes (number)',
 'Repeaters in Grade 2 of primary education, female (number)',
 'Repeaters in Grade 3 of primary education, both sexes (number)',
 'Repeaters in Grade 3 of primary education, female (number)',
 'Repeaters in Grade 4 of primary education, both sexes (number)',
 'Repeaters in Grade 4 of primary education, female (number)',
 'Repeaters in Grade 5 of primary education, both sexes (number)',
 'Repeaters in Grade 5 of primary education, female (number)',
 'Repeaters in Grade 6 of primary education, both sexes (number)',
 'Repeaters in Grade 6 of primary education, female (number)',
 'Repeaters in Grade 7 of primary education, both sexes (number)',
 'Repeaters in Grade 7 of primary education, female (number)',
 'Repeaters in lower secondary general education, all grades, both sexes (number)',
 'Repeaters in lower secondary general education, all grades, female (number)',
 'Repeaters in Grade 1 of lower secondary general education, both sexes (number)',
 'Repeaters in Grade 1 of lower secondary general education, female (number)',
 'Repeaters in Grade 2 of lower secondary general education, both sexes (number)',
 'Repeaters in Grade 2 of lower secondary general education, female (number)',
 'Repeaters in Grade 3 of lower secondary general education, both sexes (number)',
 'Repeaters in Grade 3 of lower secondary general education, female (number)',
 'Repeaters in Grade 4 of lower secondary general education, both sexes (number)',
 'Repeaters in Grade 4 of lower secondary general education, female (number)',
 'Repeaters in Grade 5 of lower secondary general education, both sexes (number)',
 'Repeaters in Grade 5 of lower secondary general education, female (number)',
 'Percentage of repeaters in Grade 1 of primary education, both sexes (%)',
 'Percentage of repeaters in Grade 1 of primary education, female (%)',
 'Percentage of repeaters in Grade 1 of primary education, male (%)',
 'Percentage of repeaters in Grade 2 of primary education, both sexes (%)',
 'Percentage of repeaters in Grade 2 of primary education, female (%)',
 'Percentage of repeaters in Grade 2 of primary education, male (%)',
 'Percentage of repeaters in Grade 3 of primary education, both sexes (%)',
 'Percentage of repeaters in Grade 3 of primary education, female (%)',
 'Percentage of repeaters in Grade 3 of primary education, male (%)',
 'Percentage of repeaters in Grade 4 of primary education, both sexes (%)',
 'Percentage of repeaters in Grade 4 of primary education, female (%)',
 'Percentage of repeaters in Grade 4 of primary education, male (%)',
 'Percentage of repeaters in Grade 5 of primary education, both sexes (%)',
 'Percentage of repeaters in Grade 5 of primary education, female (%)',
 'Percentage of repeaters in Grade 5 of primary education, male (%)',
 'Percentage of repeaters in Grade 6 of primary education, both sexes (%)',
 'Percentage of repeaters in Grade 6 of primary education, female (%)',
 'Percentage of repeaters in Grade 6 of primary education, male (%)',
 'Percentage of repeaters in Grade 7 of primary education, both sexes (%)',
 'Percentage of repeaters in Grade 7 of primary education, female (%)',
 'Percentage of repeaters in Grade 7 of primary education, male (%)',
 'Percentage of repeaters in primary education, all grades, gender parity index (GPI)',
 'Percentage of repeaters in lower secondary general education, all grades, both sexes (%)',
 'Percentage of repeaters in lower secondary general education, all grades, female (%)',
 'Percentage of repeaters in Grade 1 of lower secondary general education, both sexes (%)',
 'Percentage of repeaters in Grade 1 of lower secondary general education, female (%)',
 'Percentage of repeaters in Grade 1 of lower secondary general education, male (%)',
 'Percentage of repeaters in Grade 2 of lower secondary general education, both sexes (%)',
 'Percentage of repeaters in Grade 2 of lower secondary general education, female (%)',
 'Percentage of repeaters in Grade 2 of lower secondary general education, male (%)',
 'Percentage of repeaters in Grade 3 of lower secondary general education, both sexes (%)',
 'Percentage of repeaters in Grade 3 of lower secondary general education, female (%)',
 'Percentage of repeaters in Grade 3 of lower secondary general education, male (%)',
 'Percentage of repeaters in Grade 4 of lower secondary general education, both sexes (%)',
 'Percentage of repeaters in Grade 4 of lower secondary general education, female (%)',
 'Percentage of repeaters in Grade 4 of lower secondary general education, male (%)',
 'Percentage of repeaters in Grade 5 of lower secondary general education, both sexes (%)',
 'Percentage of repeaters in Grade 5 of lower secondary general education, female (%)',
 'Percentage of repeaters in Grade 5 of lower secondary general education, male (%)',
 'Percentage of repeaters in lower secondary education, all grades, gender parity index (GPI)',
 'Percentage of repeaters in lower secondary general education, all grades, male (%)',
 'Repetition rate in primary education (all grades), both sexes (%)',
 'Repetition rate in primary education (all grades), female (%)',
 'Repetition rate in Grade 1 of primary education, both sexes (%)',
 'Repetition rate in Grade 1 of primary education, female (%)',
 'Repetition rate in Grade 1 of primary education, male (%)',
 'Repetition rate in Grade 2 of primary education, both sexes (%)',
 'Repetition rate in Grade 2 of primary education, female (%)',
 'Repetition rate in Grade 2 of primary education, male (%)',
 'Repetition rate in Grade 3 of primary education, both sexes (%)',
 'Repetition rate in Grade 3 of primary education, female (%)',
 'Repetition rate in Grade 3 of primary education, male (%)',
 'Repetition rate in Grade 4 of primary education, both sexes (%)',
 'Repetition rate in Grade 4 of primary education, female (%)',
 'Repetition rate in Grade 4 of primary education, male (%)',
 'Repetition rate in Grade 5 of primary education, both sexes (%)',
 'Repetition rate in Grade 5 of primary education, female (%)',
 'Repetition rate in Grade 5 of primary education, male (%)',
 'Repetition rate in Grade 6 of primary education, both sexes (%)',
 'Repetition rate in Grade 6 of primary education, female (%)',
 'Repetition rate in Grade 6 of primary education, male (%)',
 'Repetition rate in Grade 7 of primary education, both sexes (%)',
 'Repetition rate in Grade 7 of primary education, female (%)',
 'Repetition rate in Grade 7 of primary education, male (%)',
 'Repetition rate in primary education (all grades), male (%)',
 'Repetition rate in lower secondary general education (all grades), both sexes (%)',
 'Repetition rate in lower secondary general education (all grades), female (%)',
 'Repetition rate in Grade 1 of lower secondary general education, both sexes (%)',
 'Repetition rate in Grade 1 of lower secondary general education, female (%)',
 'Repetition rate in Grade 1 of lower secondary general education, male (%)',
 'Repetition rate in Grade 2 of lower secondary general education, both sexes (%)',
 'Repetition rate in Grade 2 of lower secondary general education, female (%)',
 'Repetition rate in Grade 2 of lower secondary general education, male (%)',
 'Repetition rate in Grade 3 of lower secondary general education, both sexes (%)',
 'Repetition rate in Grade 3 of lower secondary general education, female (%)',
 'Repetition rate in Grade 3 of lower secondary general education, male (%)',
 'Repetition rate in Grade 4 of lower secondary general education, both sexes (%)',
 'Repetition rate in Grade 4 of lower secondary general education, female (%)',
 'Repetition rate in Grade 4 of lower secondary general education, male (%)',
 'Repetition rate in Grade 5 of lower secondary general education, both sexes (%)',
 'Repetition rate in Grade 5 of lower secondary general education, female (%)',
 'Repetition rate in Grade 5 of lower secondary general education, male (%)',
 'Repetition rate in lower secondary general education (all grades), male (%)',
 'Rate of out-of-school children of primary school age in pre-primary education, both sexes (%)',
 'Rate of out-of-school children of primary school age in pre-primary education, female (%)',
 'Rate of out-of-school children of primary school age in pre-primary education, male (%)',
 'Rate of out-of-school children of primary school age, both sexes (%)',
 'Rate of out-of-school children of primary school age, female (%)',
 'Rate of out-of-school children of primary school age, male (%)',
 'Rate of out-of-school adolescents of lower secondary school age, both sexes (%)',
 'Rate of out-of-school adolescents of lower secondary school age, female (%)',
 'Rate of out-of-school adolescents of lower secondary school age, male (%)',
 'Rate of out-of-school youth of upper secondary school age, both sexes (%)',
 'Rate of out-of-school youth of upper secondary school age, female (%)',
 'Rate of out-of-school youth of upper secondary school age, male (%)',
 'Population of the official entrance age to primary education, both sexes (number)',
 'Population of the official entrance age to primary education, female (number)',
 'Population of the official entrance age to primary education, male (number)',
 'Population of the official entrance age to secondary general education, both sexes (number)',
 'Population of the official entrance age to secondary general education, female (number)',
 'Population of the official entrance age to secondary general education, male (number)',
 'Population of the official age for post-secondary non-tertiary education, both sexes (number)',
 'Population of the official age for post-secondary non-tertiary education, female (number)',
 'Population of the official age for post-secondary non-tertiary education, male (number)',
 'Population of compulsory school age, both sexes (number)',
 'Population of compulsory school age, female (number)',
 'Population of compulsory school age, male (number)',
 'School life expectancy, pre-primary, both sexes (years)',
 'School life expectancy, pre-primary, female (years)',
 'School life expectancy, pre-primary, gender parity index (GPI)',
 'School life expectancy, pre-primary, male (years)',
 'School life expectancy, primary, both sexes (years)',
 'School life expectancy, primary, female (years)',
 'School life expectancy, primary, gender parity index (GPI)',
 'School life expectancy, primary, male (years)',
 'School life expectancy, primary and lower secondary, both sexes (years)',
 'School life expectancy, primary and lower secondary, female (years)',
 'School life expectancy, primary and lower secondary, male (years)',
 'School life expectancy, primary and secondary, both sexes (years)',
 'School life expectancy, primary and secondary, female (years)',
 'School life expectancy, primary and secondary, gender parity index (GPI)',
 'School life expectancy, primary and secondary, male (years)',
 'School life expectancy, primary to tertiary, gender parity index (GPI)',
 'School life expectancy, secondary, both sexes (years)',
 'School life expectancy, secondary, female (years)',
 'School life expectancy, secondary, gender parity index (GPI)',
 'School life expectancy, secondary, male (years)',
 'School life expectancy, post-secondary non-tertiary, both sexes (years)',
 'School life expectancy, post-secondary non-tertiary, female (years)',
 'School life expectancy, post-secondary non-tertiary, gender parity index (GPI)',
 'School life expectancy, post-secondary non-tertiary, male (years)',
 'School life expectancy, tertiary, both sexes (years)',
 'School life expectancy, tertiary, female (years)',
 'School life expectancy, tertiary, gender parity index (GPI)',
 'School life expectancy, tertiary, male (years)',
 'School life expectancy, primary and lower secondary (excluding repetition), female (years)',
 'School life expectancy, primary and lower secondary (excluding repetition), gender parity index (GPI)',
 'School life expectancy, primary and lower secondary (excluding repetition), male (years)',
 'School life expectancy, primary and lower secondary (excluding repetition), both sexes (years)',
 'Survival rate to Grade 4 of primary education, both sexes (%)',
 'Survival rate to Grade 4 of primary education, female (%)',
 'Survival rate to Grade 4 of primary education, gender parity index (GPI)',
 'Survival rate to Grade 4 of primary education, male (%)',
 'Survival rate to Grade 5 of primary education, gender parity index (GPI)',
 'Survival rate to the last grade of primary education, gender parity index (GPI)',
 'Survival rate to the last grade of lower secondary general education, female (%)',
 'Survival rate to the last grade of lower secondary general education, male (%)',
 'Survival rate to the last grade of lower secondary general education, both sexes (%)',
 'Survival rate to the last grade of lower secondary general education, gender parity index (GPI)',
 'Teachers in lower secondary education, both sexes (number)',
 'Teachers in lower secondary education, female (number)',
 'Teachers in secondary general education, both sexes (number)',
 'Teachers in secondary general education, female (number)',
 'Teachers in secondary vocational education, both sexes (number)',
 'Teachers in secondary vocational education, female (number)',
 'Teachers in upper secondary education, both sexes (number)',
 'Teachers in upper secondary education, female (number)',
 'Teachers in post-secondary non-tertiary education, both sexes (number)',
 'Teachers in post-secondary non-tertiary education, female (number)',
 'Teachers in tertiary education ISCED 5 programmes, both sexes (number)',
 'Teachers in tertiary education ISCED 5 programmes, female (number)',
 'Enrolment in tertiary education per 100,000 inhabitants, both sexes',
 'Enrolment in tertiary education per 100,000 inhabitants, female',
 'Enrolment in tertiary education per 100,000 inhabitants, male',
 'Percentage of all students in tertiary education enrolled in ISCED 5, both sexes (%)',
 'Percentage of female students in tertiary education enrolled in ISCED 5',
 'Percentage of male students in tertiary education enrolled in ISCED 5',
 'Percentage of all students in tertiary education enrolled in ISCED 6, both sexes (%)',
 'Percentage of female students in tertiary education enrolled in ISCED 6',
 'Percentage of male students in tertiary education enrolled in ISCED 6',
 'Percentage of all students in tertiary education enrolled in ISCED 8, both sexes (%)',
 'Percentage of female students in tertiary education enrolled in ISCED 8',
 'Percentage of male students in tertiary education enrolled in ISCED 8',
 'Official entrance age to pre-primary education (years)',
 'Official entrance age to upper secondary education (years)',
 'Official entrance age to post-secondary non-tertiary education (years)',
 'Theoretical duration of pre-primary education (years)',
 'Theoretical duration of post-secondary non-tertiary education (years)',
 'Effective transition rate from primary to lower secondary general education, gender parity index (GPI)',
 'Percentage of teachers in pre-primary education who are trained, both sexes (%)',
 'Percentage of female teachers in pre-primary education who are trained, female (%)',
 'Percentage of teachers in pre-primary education who are trained, gender parity index (GPI)',
 'Percentage of male teachers in pre-primary education who are trained, male (%)',
 'Percentage of teachers in primary education who are trained, gender parity index (GPI)',
 'Percentage of teachers in lower secondary education who are trained, both sexes (%)',
 'Percentage of female teachers in lower secondary education who are trained, female (%)',
 'Percentage of teachers in lower secondary education who are trained, gender parity index (GPI)',
 'Percentage of male teachers in lower secondary education who are trained, male (%)',
 'Percentage of teachers in secondary education who are trained, gender parity index (GPI)',
 'Percentage of teachers in upper secondary education who are trained, both sexes (%)',
 'Percentage of female teachers in upper secondary education who are trained, female (%)',
 'Percentage of teachers in upper secondary education who are trained, gender parity index (GPI)',
 'Percentage of male teachers in upper secondary education who are trained, male (%)',
 'Percentage of students enrolled in primary education who are under-age, both sexes (%)',
 'Percentage of female students enrolled in primary education who are under-age, female (%)',
 'Percentage of male students enrolled in primary education who are under-age, male (%)',
 'Under-age enrolment ratio in primary education, both sexes (%)',
 'Under-age enrolment ratio in primary education, female (%)',
 'Under-age enrolment ratio in primary education, male (%)',
 'Under-age enrolment ratio in secondary education, both sexes (%)',
 'Under-age enrolment ratio in secondary education, female (%)',
 'Under-age enrolment ratio in secondary education, male (%)',
 'Government expenditure on pre-primary education as % of GDP (%)',
 'Government expenditure in pre-primary institutions as % of GDP (%)',
 'Government expenditure on primary education as % of GDP (%)',
 'Government expenditure in primary institutions as % of GDP (%)',
 'Government expenditure on secondary education as % of GDP (%)',
 'Government expenditure on post-secondary non-tertiary education as % of GDP (%)',
 'Government expenditure in post-secondary non-tertiary institutions as % of GDP (%)',
 'Government expenditure on tertiary education as % of GDP (%)',
 'Government expenditure in educational institutions as % of GDP (%)',
 'Expenditure on pre-primary as % of total government expenditure (%)',
 'Expenditure on primary as % of total government expenditure (%)',
 'Expenditure on lower secondary as % of total government expenditure (%)',
 'Expenditure on secondary as % of total government expenditure (%)',
 ...]

Sélection des variables

In [ ]:
# fonction filtrage ligne (fonctionnel)

df4[df4['Indicator Name'].isin(['GDP, PPP (current international $)', 'GDP, PPP (constant 2011 international $)', 'GDP per capita, PPP (current international $)', 'GDP per capita, PPP (constant 2011 international $)', 'Government expenditure on secondary education as % of GDP (%)', 'Government expenditure on tertiary education as % of GDP (%)', 'Internet users (per 100 people)', 'Population growth (annual %)', 'Population, total'])]
df5 = df4[df4['Indicator Name'].isin(['GDP, PPP (current international $)', 'GDP, PPP (constant 2011 international $)', 'GDP per capita, PPP (current international $)', 'GDP per capita, PPP (constant 2011 international $)', 'Government expenditure on secondary education as % of GDP (%)', 'Government expenditure on tertiary education as % of GDP (%)', 'Internet users (per 100 people)', 'Population growth (annual %)', 'Population, total'])]
In [ ]:
df5.shape
Out[ ]:
(1337, 40)
In [ ]:
# Je crée une liste des années que je souhaite sélectionner

liste_annees = ['2000', '2001', '2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009', '2010', '2011', '2012', '2013', '2014', '2015', '2016', '2017']
In [ ]:
# liste de variables à étudier

var_to_study = ('GDP, PPP (current international $)', 'GDP, PPP (constant 2011 international $)', 'GDP per capita, PPP (current international $)', 'GDP per capita, PPP (constant 2011 international $)', 'Government expenditure on secondary education as % of GDP (%)', 'Government expenditure on tertiary education as % of GDP (%)', 'Internet users (per 100 people)', 'Population growth (annual %)', 'Population, total')
In [ ]:
# Je vérifie que ma fonction melt est bien écrite

pd.melt(df5, id_vars=['Country Name'], value_vars=liste_annees, var_name='Année')
Out[ ]:
Country Name Année value
0 Afghanistan 2000 NaN
1 Angola 2000 0.105046
2 Albania 2000 0.114097
3 United Arab Emirates 2000 23.625301
4 Argentina 2000 7.038683
... ... ... ...
24061 Slovak Republic 2017 NaN
24062 Tajikistan 2017 NaN
24063 Tunisia 2017 NaN
24064 Ukraine 2017 NaN
24065 South Africa 2017 NaN

24066 rows × 3 columns

In [ ]:
# J'organise chaque variable

df5_melted = df5.melt(id_vars=['Country Name','Region', 'Income Group', 'Indicator Name'], value_vars=liste_annees, var_name='Année')
df5_Income_Group = df5.melt(id_vars=['Country Name'], value_vars='Income Group', var_name='Niveau de richesse')
In [ ]:
df5_melted
Out[ ]:
Country Name Region Income Group Indicator Name Année value
0 Afghanistan South Asia Low income Internet users (per 100 people) 2000 NaN
1 Angola Sub-Saharan Africa Upper middle income Internet users (per 100 people) 2000 0.105046
2 Albania Europe & Central Asia Upper middle income Internet users (per 100 people) 2000 0.114097
3 United Arab Emirates Middle East & North Africa High income: nonOECD Internet users (per 100 people) 2000 23.625301
4 Argentina Latin America & Caribbean Upper middle income Internet users (per 100 people) 2000 7.038683
... ... ... ... ... ... ...
24061 Slovak Republic Europe & Central Asia High income: OECD Government expenditure on tertiary education a... 2017 NaN
24062 Tajikistan Europe & Central Asia Low income Government expenditure on tertiary education a... 2017 NaN
24063 Tunisia Middle East & North Africa Upper middle income Government expenditure on tertiary education a... 2017 NaN
24064 Ukraine Europe & Central Asia Lower middle income Government expenditure on tertiary education a... 2017 NaN
24065 South Africa Sub-Saharan Africa Upper middle income Government expenditure on tertiary education a... 2017 NaN

24066 rows × 6 columns

In [ ]:
# Je trie mon df par Country Name et par Année

df5_melted.sort_values(by=['Country Name', 'Année'])
Out[ ]:
Country Name Region Income Group Indicator Name Année value
0 Afghanistan South Asia Low income Internet users (per 100 people) 2000 NaN
185 Afghanistan South Asia Low income GDP, PPP (current international $) 2000 NaN
367 Afghanistan South Asia Low income GDP, PPP (constant 2011 international $) 2000 NaN
552 Afghanistan South Asia Low income GDP per capita, PPP (current international $) 2000 NaN
734 Afghanistan South Asia Low income GDP per capita, PPP (constant 2011 internation... 2000 NaN
... ... ... ... ... ... ...
23280 Zimbabwe Sub-Saharan Africa Low income GDP, PPP (constant 2011 international $) 2017 NaN
23462 Zimbabwe Sub-Saharan Africa Low income GDP per capita, PPP (current international $) 2017 NaN
23647 Zimbabwe Sub-Saharan Africa Low income GDP per capita, PPP (constant 2011 internation... 2017 NaN
23836 Zimbabwe Sub-Saharan Africa Low income Population growth (annual %) 2017 NaN
24025 Zimbabwe Sub-Saharan Africa Low income Population, total 2017 NaN

24066 rows × 6 columns

In [ ]:
# Sélection d'un pays

df5_melted[df5_melted["Country Name"] =='Afghanistan']
Out[ ]:
Country Name Region Income Group Indicator Name Année value
0 Afghanistan South Asia Low income Internet users (per 100 people) 2000 NaN
185 Afghanistan South Asia Low income GDP, PPP (current international $) 2000 NaN
367 Afghanistan South Asia Low income GDP, PPP (constant 2011 international $) 2000 NaN
552 Afghanistan South Asia Low income GDP per capita, PPP (current international $) 2000 NaN
734 Afghanistan South Asia Low income GDP per capita, PPP (constant 2011 internation... 2000 NaN
... ... ... ... ... ... ...
23096 Afghanistan South Asia Low income GDP, PPP (constant 2011 international $) 2017 NaN
23281 Afghanistan South Asia Low income GDP per capita, PPP (current international $) 2017 NaN
23463 Afghanistan South Asia Low income GDP per capita, PPP (constant 2011 internation... 2017 NaN
23648 Afghanistan South Asia Low income Population growth (annual %) 2017 NaN
23837 Afghanistan South Asia Low income Population, total 2017 NaN

126 rows × 6 columns

In [ ]:
# Je cherche un angle d'attaque pour filtrer mon dataframe


df5_melted["Income Group"].unique().tolist()
Out[ ]:
['Low income',
 'Upper middle income',
 'High income: nonOECD',
 'Lower middle income',
 'High income: OECD']

Sélection des pays à haut niveau de revenu

Je remarque que les mots 'High Income' me serait utile pour mon filtrage.

In [ ]:
# filter by string / filtre par mot-clé

df5_melted[df5_melted["Income Group"].str.contains('High income')]
df5_High_Income = df5_melted[df5_melted["Income Group"].str.contains('High income')]
df5_High_Income.head()
Out[ ]:
Country Name Region Income Group Indicator Name Année value
3 United Arab Emirates Middle East & North Africa High income: nonOECD Internet users (per 100 people) 2000 23.625301
6 Antigua and Barbuda Latin America & Caribbean High income: nonOECD Internet users (per 100 people) 2000 6.482226
7 Australia East Asia & Pacific High income: OECD Internet users (per 100 people) 2000 46.756116
8 Austria Europe & Central Asia High income: OECD Internet users (per 100 people) 2000 33.730133
11 Belgium Europe & Central Asia High income: OECD Internet users (per 100 people) 2000 29.431692
In [ ]:
df5_High_Income['Indicator Name'].unique().tolist()
Out[ ]:
['Internet users (per 100 people)',
 'GDP, PPP (current international $)',
 'GDP, PPP (constant 2011 international $)',
 'GDP per capita, PPP (current international $)',
 'GDP per capita, PPP (constant 2011 international $)',
 'Population growth (annual %)',
 'Population, total',
 'Government expenditure on secondary education as % of GDP (%)',
 'Government expenditure on tertiary education as % of GDP (%)']
In [ ]:
high_income_list = df5_High_Income['Country Name'].unique().tolist()
In [ ]:
high_income_list
Out[ ]:
['United Arab Emirates',
 'Antigua and Barbuda',
 'Australia',
 'Austria',
 'Belgium',
 'Bahrain',
 'Barbados',
 'Brunei Darussalam',
 'Canada',
 'Switzerland',
 'Chile',
 'Cyprus',
 'Czech Republic',
 'Germany',
 'Denmark',
 'Spain',
 'Estonia',
 'Finland',
 'France',
 'United Kingdom',
 'Equatorial Guinea',
 'Greece',
 'Hong Kong SAR, China',
 'Croatia',
 'Ireland',
 'Iceland',
 'Israel',
 'Italy',
 'Japan',
 'St. Kitts and Nevis',
 'Korea, Rep.',
 'Kuwait',
 'Lithuania',
 'Luxembourg',
 'Latvia',
 'Macao SAR, China',
 'Malta',
 'Netherlands',
 'Norway',
 'New Zealand',
 'Oman',
 'Poland',
 'Puerto Rico',
 'Portugal',
 'Qatar',
 'Russian Federation',
 'Saudi Arabia',
 'Singapore',
 'Slovak Republic',
 'Slovenia',
 'Sweden',
 'Trinidad and Tobago',
 'Uruguay',
 'United States',
 'Bahamas, The']
In [ ]:
df5_melted_High_Income = df5_melted[df5_melted["Country Name"].isin(high_income_list)]
In [ ]:
df5_melted_High_Income
Out[ ]:
Country Name Region Income Group Indicator Name Année value
3 United Arab Emirates Middle East & North Africa High income: nonOECD Internet users (per 100 people) 2000 23.625301
6 Antigua and Barbuda Latin America & Caribbean High income: nonOECD Internet users (per 100 people) 2000 6.482226
7 Australia East Asia & Pacific High income: OECD Internet users (per 100 people) 2000 46.756116
8 Austria Europe & Central Asia High income: OECD Internet users (per 100 people) 2000 33.730133
11 Belgium Europe & Central Asia High income: OECD Internet users (per 100 people) 2000 29.431692
... ... ... ... ... ... ...
24014 United States North America High income: OECD Population, total 2017 NaN
24037 Latvia Europe & Central Asia High income: nonOECD Government expenditure on secondary education ... 2017 NaN
24042 Slovak Republic Europe & Central Asia High income: OECD Government expenditure on secondary education ... 2017 NaN
24057 Latvia Europe & Central Asia High income: nonOECD Government expenditure on tertiary education a... 2017 NaN
24061 Slovak Republic Europe & Central Asia High income: OECD Government expenditure on tertiary education a... 2017 NaN

6804 rows × 6 columns

In [ ]:
# J'affiche les variables que je souhaite étudier. Je filtre mon dataframe par indicateur à étudier.

df5_melted_High_Income['Indicator Name'].unique().tolist()
Out[ ]:
['Internet users (per 100 people)',
 'GDP, PPP (current international $)',
 'GDP, PPP (constant 2011 international $)',
 'GDP per capita, PPP (current international $)',
 'GDP per capita, PPP (constant 2011 international $)',
 'Population growth (annual %)',
 'Population, total',
 'Government expenditure on secondary education as % of GDP (%)',
 'Government expenditure on tertiary education as % of GDP (%)']

Création des dataframes liés au variables retenues, au niveau mondial

Etude du PIB courant

In [ ]:
df_PIB_valeur_courante = df5_melted_High_Income[ df5_melted_High_Income['Indicator Name'] == 'GDP, PPP (current international $)' ]
df_PIB_valeur_courante.head()
Out[ ]:
Country Name Region Income Group Indicator Name Année value
188 United Arab Emirates Middle East & North Africa High income: nonOECD GDP, PPP (current international $) 2000 2.581316e+11
191 Antigua and Barbuda Latin America & Caribbean High income: nonOECD GDP, PPP (current international $) 2000 1.252708e+09
192 Australia East Asia & Pacific High income: OECD GDP, PPP (current international $) 2000 5.057566e+11
193 Austria Europe & Central Asia High income: OECD GDP, PPP (current international $) 2000 2.351994e+11
196 Belgium Europe & Central Asia High income: OECD GDP, PPP (current international $) 2000 2.866961e+11

Etude du PIB constant

In [ ]:
df_PIB_valeur_constante_2011 = df5_melted_High_Income[ df5_melted_High_Income['Indicator Name'] == 'GDP, PPP (constant 2011 international $)' ]
df_PIB_valeur_constante_2011.head()
Out[ ]:
Country Name Region Income Group Indicator Name Année value
370 United Arab Emirates Middle East & North Africa High income: nonOECD GDP, PPP (constant 2011 international $) 2000 3.256653e+11
373 Antigua and Barbuda Latin America & Caribbean High income: nonOECD GDP, PPP (constant 2011 international $) 2000 1.580448e+09
374 Australia East Asia & Pacific High income: OECD GDP, PPP (constant 2011 international $) 2000 6.757446e+11
375 Austria Europe & Central Asia High income: OECD GDP, PPP (constant 2011 international $) 2000 3.124549e+11
378 Belgium Europe & Central Asia High income: OECD GDP, PPP (constant 2011 international $) 2000 3.812301e+11

Etude du PIB courant / habitant

In [ ]:
df_PIB_par_habitant_courant = df5_melted_High_Income[ df5_melted_High_Income['Indicator Name'] == 'GDP per capita, PPP (current international $)' ]
df_PIB_par_habitant_courant.head()
Out[ ]:
Country Name Region Income Group Indicator Name Année value
555 United Arab Emirates Middle East & North Africa High income: nonOECD GDP per capita, PPP (current international $) 2000 81818.620517
558 Antigua and Barbuda Latin America & Caribbean High income: nonOECD GDP per capita, PPP (current international $) 2000 14987.411027
559 Australia East Asia & Pacific High income: OECD GDP per capita, PPP (current international $) 2000 26406.130951
560 Austria Europe & Central Asia High income: OECD GDP per capita, PPP (current international $) 2000 29357.485725
563 Belgium Europe & Central Asia High income: OECD GDP per capita, PPP (current international $) 2000 27966.938804

Etude du PIB constant / habitant

In [ ]:
df_PIB_par_habitant_constant_2011 = df5_melted_High_Income[ df5_melted_High_Income['Indicator Name'] == 'GDP per capita, PPP (constant 2011 international $)' ]
df_PIB_par_habitant_constant_2011.head()
Out[ ]:
Country Name Region Income Group Indicator Name Année value
737 United Arab Emirates Middle East & North Africa High income: nonOECD GDP per capita, PPP (constant 2011 internation... 2000 103224.426650
740 Antigua and Barbuda Latin America & Caribbean High income: nonOECD GDP per capita, PPP (constant 2011 internation... 2000 18908.494185
741 Australia East Asia & Pacific High income: OECD GDP per capita, PPP (constant 2011 internation... 2000 35281.395362
742 Austria Europe & Central Asia High income: OECD GDP per capita, PPP (constant 2011 internation... 2000 39000.480953
745 Belgium Europe & Central Asia High income: OECD GDP per capita, PPP (constant 2011 internation... 2000 37188.643693

Etude des dépenses d'éducation secondaire en pourcentage du PIB

In [ ]:
df_Dépense_educ_2ndaire_pourcent_du_PIB = df5_melted_High_Income[ df5_melted_High_Income['Indicator Name'] == 'Government expenditure on secondary education as % of GDP (%)' ]
df_Dépense_educ_2ndaire_pourcent_du_PIB.head()
Out[ ]:
Country Name Region Income Group Indicator Name Année value
1308 Latvia Europe & Central Asia High income: nonOECD Government expenditure on secondary education ... 2000 2.57449
1313 Slovak Republic Europe & Central Asia High income: OECD Government expenditure on secondary education ... 2000 1.97573
2645 Latvia Europe & Central Asia High income: nonOECD Government expenditure on secondary education ... 2001 2.81808
2650 Slovak Republic Europe & Central Asia High income: OECD Government expenditure on secondary education ... 2001 2.02052
3982 Latvia Europe & Central Asia High income: nonOECD Government expenditure on secondary education ... 2002 2.96802

Etude des dépenses d'éducation supérieure en pourcentage du PIB

In [ ]:
df_Dépense_educ_tertiaire_pourcent_du_PIB = df5_melted_High_Income[ df5_melted_High_Income['Indicator Name'] == 'Government expenditure on tertiary education as % of GDP (%)' ]
df_Dépense_educ_tertiaire_pourcent_du_PIB.head()
Out[ ]:
Country Name Region Income Group Indicator Name Année value
1328 Latvia Europe & Central Asia High income: nonOECD Government expenditure on tertiary education a... 2000 0.86024
1332 Slovak Republic Europe & Central Asia High income: OECD Government expenditure on tertiary education a... 2000 0.71140
2665 Latvia Europe & Central Asia High income: nonOECD Government expenditure on tertiary education a... 2001 0.85286
2669 Slovak Republic Europe & Central Asia High income: OECD Government expenditure on tertiary education a... 2001 0.80926
4002 Latvia Europe & Central Asia High income: nonOECD Government expenditure on tertiary education a... 2002 0.86497

Taux d'utilisation d'internet

In [ ]:
df_internet_users_pourcent = df5_melted_High_Income[ df5_melted_High_Income['Indicator Name'] == 'Internet users (per 100 people)' ]
df_internet_users_pourcent.head()                               
Out[ ]:
Country Name Region Income Group Indicator Name Année value
3 United Arab Emirates Middle East & North Africa High income: nonOECD Internet users (per 100 people) 2000 23.625301
6 Antigua and Barbuda Latin America & Caribbean High income: nonOECD Internet users (per 100 people) 2000 6.482226
7 Australia East Asia & Pacific High income: OECD Internet users (per 100 people) 2000 46.756116
8 Austria Europe & Central Asia High income: OECD Internet users (per 100 people) 2000 33.730133
11 Belgium Europe & Central Asia High income: OECD Internet users (per 100 people) 2000 29.431692

Taux de croissance de la population

In [ ]:
df_pop_growth_pourcent = df5_melted_High_Income[ df5_melted_High_Income['Indicator Name'] == 'Population growth (annual %)' ]
df_pop_growth_pourcent.head()
Out[ ]:
Country Name Region Income Group Indicator Name Année value
922 United Arab Emirates Middle East & North Africa High income: nonOECD Population growth (annual %) 2000 5.430624
925 Antigua and Barbuda Latin America & Caribbean High income: nonOECD Population growth (annual %) 2000 2.119597
926 Australia East Asia & Pacific High income: OECD Population growth (annual %) 2000 1.192272
927 Austria Europe & Central Asia High income: OECD Population growth (annual %) 2000 0.240467
930 Belgium Europe & Central Asia High income: OECD Population growth (annual %) 2000 0.242518

Total de la population

In [ ]:
df_total_population = df5_melted_High_Income[ df5_melted_High_Income['Indicator Name'] == 'Population, total' ]
df_total_population.head()
Out[ ]:
Country Name Region Income Group Indicator Name Année value
1111 United Arab Emirates Middle East & North Africa High income: nonOECD Population, total 2000 3154925.0
1114 Antigua and Barbuda Latin America & Caribbean High income: nonOECD Population, total 2000 83584.0
1115 Australia East Asia & Pacific High income: OECD Population, total 2000 19153000.0
1116 Austria Europe & Central Asia High income: OECD Population, total 2000 8011566.0
1119 Belgium Europe & Central Asia High income: OECD Population, total 2000 10251250.0
Dataframes à analyser df_PIB_valeur_courante df_PIB_valeur_constante_2011 df_PIB_par_habitant_courant df_PIB_par_habitant_constant_2011 df_Dépense_educ_2ndaire_pourcent_du_PIB df_Dépense_educ_tertiaire_pourcent_du_PIB df_internet_users_pourcent df_pop_growth_pourcent df_total_population

Représentation graphique des dataframes précédents

Afin de faciliter la compréhension graphique, je choisis de regrouper les pays par région.

In [ ]:
sns.catplot(x='Année',y='value',data=df_PIB_valeur_courante, hue='Region', kind='point', height=10)
Out[ ]:
<seaborn.axisgrid.FacetGrid at 0x127801e6490>
In [ ]:
sns.catplot(x='Année',y='value',data=df_PIB_valeur_constante_2011, hue='Region', kind='point', height=10)
Out[ ]:
<seaborn.axisgrid.FacetGrid at 0x127801ffaf0>

Analyse

En comparant les deux graphiques ci-dessus, nous pouvons remarquer que l'Amérique du nord comporte le PIB le plus important et de loin, de 2000 à 2017.

In [ ]:
sns.catplot(x='Année',y='value',data=df_PIB_par_habitant_courant, hue='Region', kind='point', height=10)
Out[ ]:
<seaborn.axisgrid.FacetGrid at 0x12781fde820>

Analyse

Bien que l'amérique du Nord comporte le PIB le plus important du point de vue globale (graphique 1 et 2) nous remarquons qu'elle ne représente pas la région du monde la plus riche.

Le PIB par habitant vise à mettre en avant le niveau de richesse du pays, rapporté au nombre d'habitants. Il constitue donc un indicateur du niveau de vie des habitants, aussi appelé pouvoir d'achat. Toutefois, il ne permet pas de prendre en compte les inégalités de richesses.

Ainsi nous remarquons qu'entre 2012 et 2017, l'Afrique centrale et du nord s'est vue rattrapé par la région d'Asie de l'Est/Pacifique à partir de 2012. L'Amérique du Nord est quant à elle 3ème de ce point de vue.

In [ ]:
sns.catplot(x='Année',y='value',data=df_PIB_par_habitant_constant_2011, hue='Region', kind='point', height=10)
Out[ ]:
<seaborn.axisgrid.FacetGrid at 0x1278201c100>

Analyse

En supprimant l'effet de l'inflation, nous constatons que la tendance semble se confirmer.

In [ ]:
sns.catplot(x='Année',y='value',data=df_Dépense_educ_2ndaire_pourcent_du_PIB, hue='Region', kind='point', height=10)
Out[ ]:
<seaborn.axisgrid.FacetGrid at 0x12781fdf1c0>

Analyse

Parmis l'ensemble des pays comportant un haut niveau de revenus, nous remarquons que seule région d'Europe et Asie centrale se démarque. Elle semble répondre à la fois aux critères de niveau de richesse élevés, dans chaque pays concernés et être la seule à investir dans l'éducation secondaire. Toutefois, ceci étant peu probable, il est possible que les données n'aient pas été renseignées pour les autres régions du monde.

Nous pouvons constater qu'il y a une baisse des dépenses de l'éducation secondaire en pourcentage du PIB. La question est : Pourquoi ?

Nous allons tenter d'y répondre grâce au graphique suivant.

In [ ]:
sns.catplot(x='Année',y='value',data=df_Dépense_educ_tertiaire_pourcent_du_PIB, hue='Region', kind='point', height=10)
Out[ ]:
<seaborn.axisgrid.FacetGrid at 0x1278af83be0>

Analyse

En étudiant ce graphique, nous constatons qu'il y a eu une nette hausse des investissements dans l'éducation supérieure au sein de la région Europe et Asie centrale. Nous avons ainsi une clé qui pourrait expliquer la baisse des investissements, en pourcentage, de l'éducation secondaire. Il semble que la part allouée se soit développée davantage dans l'éducation supérieure, ce qui a créé une baisse, en proportion, du taux d'investissement dans l'éducation secondaire. De plus, les études supérieures ont tendance à être plus coûteuse, ce qui influence davantage ces chiffres.

In [ ]:
sns.catplot(x='Année',y='value',data=df_internet_users_pourcent, hue='Region', kind='point', height=10)
Out[ ]:
<seaborn.axisgrid.FacetGrid at 0x1278af15070>

Analyse

D'après ce graphique représentant le taux d'utilisateurs d'internet par région, nous avons, par ordre d'importance : L'Est de l'Asie et Pacifique, puis ex-aequo : L'Amérique du nord, L'Europe et l'Asie Centrale, Le Centre est et l'Afrique du Nord.

In [ ]:
sns.catplot(x='Année',y='value',data=df_pop_growth_pourcent, hue='Region', kind='point', height=10)
Out[ ]:
<seaborn.axisgrid.FacetGrid at 0x1278b024d60>
In [ ]:
sns.catplot(x='Année',y='value',data=df_total_population, hue='Region', kind='point', height=10)
Out[ ]:
<seaborn.axisgrid.FacetGrid at 0x1278b04bca0>

Analyse

Ce graphique représente le niveau de population par région du monde. Il confirme l'idée selon laquelle le PIB par habitant en Amérique du Nord est fortement dilué par rapport aux autres régions du monde.

Etude des variables par région

Je regroupe mes données par région pour préparer mes données

Asie de l'Est et pacifique

In [ ]:
# Je veux faire apparaître les pays d'Asie de l'Est et du pacifique, comportant un haut niveau de revenu.


df5_High_Income_final = df5_High_Income[df5_High_Income["Region"].str.contains('East Asia & Pacific')]
df5_High_Income_final.head()
Out[ ]:
Country Name Region Income Group Indicator Name Année value
7 Australia East Asia & Pacific High income: OECD Internet users (per 100 people) 2000 46.756116
23 Brunei Darussalam East Asia & Pacific High income: nonOECD Internet users (per 100 people) 2000 8.996285
69 Hong Kong SAR, China East Asia & Pacific High income: nonOECD Internet users (per 100 people) 2000 27.827761
84 Japan East Asia & Pacific High income: OECD Internet users (per 100 people) 2000 29.990740
91 Korea, Rep. East Asia & Pacific High income: OECD Internet users (per 100 people) 2000 44.700000
In [ ]:
# Je souhaite afficher les variables à étudier pour la région concernée


df5_High_Income_final['Indicator Name'].unique().tolist()
Out[ ]:
['Internet users (per 100 people)',
 'GDP, PPP (current international $)',
 'GDP, PPP (constant 2011 international $)',
 'GDP per capita, PPP (current international $)',
 'GDP per capita, PPP (constant 2011 international $)',
 'Population growth (annual %)',
 'Population, total']

Etude des indicateurs pour la région : Asie de l'Est et pacifique

Etude du PIB courant

In [ ]:
df_PIB_courant = df5_High_Income_final[ df5_High_Income_final['Indicator Name'] == 'GDP, PPP (current international $)' ]
df_PIB_courant.head()
Out[ ]:
Country Name Region Income Group Indicator Name Année value
192 Australia East Asia & Pacific High income: OECD GDP, PPP (current international $) 2000 5.057566e+11
207 Brunei Darussalam East Asia & Pacific High income: nonOECD GDP, PPP (current international $) 2000 2.167227e+10
252 Hong Kong SAR, China East Asia & Pacific High income: nonOECD GDP, PPP (current international $) 2000 1.797056e+11
267 Japan East Asia & Pacific High income: OECD GDP, PPP (current international $) 2000 3.398784e+12
274 Korea, Rep. East Asia & Pacific High income: OECD GDP, PPP (current international $) 2000 8.500516e+11

Etude du PIB constant

In [ ]:
df_PIB_constant_2011 = df5_High_Income_final[ df5_High_Income_final['Indicator Name'] == 'GDP, PPP (constant 2011 international $)' ]
df_PIB_constant_2011.head()
Out[ ]:
Country Name Region Income Group Indicator Name Année value
374 Australia East Asia & Pacific High income: OECD GDP, PPP (constant 2011 international $) 2000 6.757446e+11
391 Brunei Darussalam East Asia & Pacific High income: nonOECD GDP, PPP (constant 2011 international $) 2000 2.734228e+10
437 Hong Kong SAR, China East Asia & Pacific High income: nonOECD GDP, PPP (constant 2011 international $) 2000 2.267212e+11
452 Japan East Asia & Pacific High income: OECD GDP, PPP (constant 2011 international $) 2000 4.296406e+12
459 Korea, Rep. East Asia & Pacific High income: OECD GDP, PPP (constant 2011 international $) 2000 9.757370e+11

Etude du PIB courant / habitant

In [ ]:
df_PIB_par_habitant_courant = df5_High_Income_final[ df5_High_Income_final['Indicator Name'] == 'GDP per capita, PPP (current international $)' ]
df_PIB_par_habitant_courant.head()
Out[ ]:
Country Name Region Income Group Indicator Name Année value
559 Australia East Asia & Pacific High income: OECD GDP per capita, PPP (current international $) 2000 26406.130951
574 Brunei Darussalam East Asia & Pacific High income: nonOECD GDP per capita, PPP (current international $) 2000 65034.834659
619 Hong Kong SAR, China East Asia & Pacific High income: nonOECD GDP per capita, PPP (current international $) 2000 26962.587770
634 Japan East Asia & Pacific High income: OECD GDP per capita, PPP (current international $) 2000 26795.207427
641 Korea, Rep. East Asia & Pacific High income: OECD GDP per capita, PPP (current international $) 2000 18083.084102

Etude du PIB constant / habitant

In [ ]:
df_PIB_par_habitant_constant_2011 = df5_High_Income_final[ df5_High_Income_final['Indicator Name'] == 'GDP per capita, PPP (constant 2011 international $)' ]
df_PIB_par_habitant_constant_2011.head()
Out[ ]:
Country Name Region Income Group Indicator Name Année value
741 Australia East Asia & Pacific High income: OECD GDP per capita, PPP (constant 2011 internation... 2000 35281.395362
758 Brunei Darussalam East Asia & Pacific High income: nonOECD GDP per capita, PPP (constant 2011 internation... 2000 82049.580860
804 Hong Kong SAR, China East Asia & Pacific High income: nonOECD GDP per capita, PPP (constant 2011 internation... 2000 34016.677940
819 Japan East Asia & Pacific High income: OECD GDP per capita, PPP (constant 2011 internation... 2000 33871.843545
826 Korea, Rep. East Asia & Pacific High income: OECD GDP per capita, PPP (constant 2011 internation... 2000 20756.779897

Taux de croissance de la population

In [ ]:
df_pop_growth = df5_High_Income_final[ df5_High_Income_final['Indicator Name'] == 'Population growth (annual %)' ]
df_pop_growth.head()
Out[ ]:
Country Name Region Income Group Indicator Name Année value
926 Australia East Asia & Pacific High income: OECD Population growth (annual %) 2000 1.192272
942 Brunei Darussalam East Asia & Pacific High income: nonOECD Population growth (annual %) 2000 2.108246
988 Hong Kong SAR, China East Asia & Pacific High income: nonOECD Population growth (annual %) 2000 0.881594
1003 Japan East Asia & Pacific High income: OECD Population growth (annual %) 2000 0.167276
1010 Korea, Rep. East Asia & Pacific High income: OECD Population growth (annual %) 2000 0.836181

Total de la population

In [ ]:
df_pop_total = df5_High_Income_final[ df5_High_Income_final['Indicator Name'] == 'Population, total' ]
df_pop_total.head()
Out[ ]:
Country Name Region Income Group Indicator Name Année value
1115 Australia East Asia & Pacific High income: OECD Population, total 2000 19153000.0
1131 Brunei Darussalam East Asia & Pacific High income: nonOECD Population, total 2000 333241.0
1177 Hong Kong SAR, China East Asia & Pacific High income: nonOECD Population, total 2000 6665000.0
1192 Japan East Asia & Pacific High income: OECD Population, total 2000 126843000.0
1199 Korea, Rep. East Asia & Pacific High income: OECD Population, total 2000 47008111.0
Je filtre mes données pour étudier la région Asie de l'Est et Pacifique
In [ ]:
df_final_East_Asia_Pacific = df5_High_Income_final[df5_High_Income_final['Region'] == 'East Asia & Pacific']
df_final_East_Asia_Pacific
Out[ ]:
Country Name Region Income Group Indicator Name Année value
7 Australia East Asia & Pacific High income: OECD Internet users (per 100 people) 2000 46.756116
23 Brunei Darussalam East Asia & Pacific High income: nonOECD Internet users (per 100 people) 2000 8.996285
69 Hong Kong SAR, China East Asia & Pacific High income: nonOECD Internet users (per 100 people) 2000 27.827761
84 Japan East Asia & Pacific High income: OECD Internet users (per 100 people) 2000 29.990740
91 Korea, Rep. East Asia & Pacific High income: OECD Internet users (per 100 people) 2000 44.700000
... ... ... ... ... ... ...
23921 Japan East Asia & Pacific High income: OECD Population, total 2017 NaN
23928 Korea, Rep. East Asia & Pacific High income: OECD Population, total 2017 NaN
23940 Macao SAR, China East Asia & Pacific High income: nonOECD Population, total 2017 NaN
23965 New Zealand East Asia & Pacific High income: OECD Population, total 2017 NaN
23985 Singapore East Asia & Pacific High income: nonOECD Population, total 2017 NaN

1008 rows × 6 columns

In [ ]:
df_final_East_Asia_Pacific['Indicator Name'].unique().tolist()
Out[ ]:
['Internet users (per 100 people)',
 'GDP, PPP (current international $)',
 'GDP, PPP (constant 2011 international $)',
 'GDP per capita, PPP (current international $)',
 'GDP per capita, PPP (constant 2011 international $)',
 'Population growth (annual %)',
 'Population, total']

Représentation graphique

Nous allons maintenant étudier les données des pays et représentation graphique par indicateur, pour la région 'East Asia & Pacific'.

In [ ]:
df_East_Asia_Pacific_Internet_users = df_final_East_Asia_Pacific[df_final_East_Asia_Pacific['Indicator Name'] == 'Internet users (per 100 people)']
df_East_Asia_Pacific_Internet_users.head()
Out[ ]:
Country Name Region Income Group Indicator Name Année value
7 Australia East Asia & Pacific High income: OECD Internet users (per 100 people) 2000 46.756116
23 Brunei Darussalam East Asia & Pacific High income: nonOECD Internet users (per 100 people) 2000 8.996285
69 Hong Kong SAR, China East Asia & Pacific High income: nonOECD Internet users (per 100 people) 2000 27.827761
84 Japan East Asia & Pacific High income: OECD Internet users (per 100 people) 2000 29.990740
91 Korea, Rep. East Asia & Pacific High income: OECD Internet users (per 100 people) 2000 44.700000
In [ ]:
sns.catplot(x='Année', y='value', data=df_East_Asia_Pacific_Internet_users, hue='Country Name', col='Indicator Name', kind='point', height = 10)
Out[ ]:
<seaborn.axisgrid.FacetGrid at 0x127801a6190>
In [ ]:
df_East_Asia_Pacific_PIB_courant = df_final_East_Asia_Pacific[df_final_East_Asia_Pacific['Indicator Name'] == 'GDP, PPP (current international $)']
df_East_Asia_Pacific_PIB_courant.head()
Out[ ]:
Country Name Region Income Group Indicator Name Année value
192 Australia East Asia & Pacific High income: OECD GDP, PPP (current international $) 2000 5.057566e+11
207 Brunei Darussalam East Asia & Pacific High income: nonOECD GDP, PPP (current international $) 2000 2.167227e+10
252 Hong Kong SAR, China East Asia & Pacific High income: nonOECD GDP, PPP (current international $) 2000 1.797056e+11
267 Japan East Asia & Pacific High income: OECD GDP, PPP (current international $) 2000 3.398784e+12
274 Korea, Rep. East Asia & Pacific High income: OECD GDP, PPP (current international $) 2000 8.500516e+11
In [ ]:
sns.catplot(x='Année', y='value', data=df_East_Asia_Pacific_PIB_courant, hue='Country Name', col='Indicator Name', kind='point', height = 10)
Out[ ]:
<seaborn.axisgrid.FacetGrid at 0x127ca4b0460>
In [ ]:
df_East_Asia_Pacific_PIB_constant_2011 = df_final_East_Asia_Pacific[df_final_East_Asia_Pacific['Indicator Name'] == 'GDP, PPP (constant 2011 international $)']
df_East_Asia_Pacific_PIB_constant_2011.head()
Out[ ]:
Country Name Region Income Group Indicator Name Année value
374 Australia East Asia & Pacific High income: OECD GDP, PPP (constant 2011 international $) 2000 6.757446e+11
391 Brunei Darussalam East Asia & Pacific High income: nonOECD GDP, PPP (constant 2011 international $) 2000 2.734228e+10
437 Hong Kong SAR, China East Asia & Pacific High income: nonOECD GDP, PPP (constant 2011 international $) 2000 2.267212e+11
452 Japan East Asia & Pacific High income: OECD GDP, PPP (constant 2011 international $) 2000 4.296406e+12
459 Korea, Rep. East Asia & Pacific High income: OECD GDP, PPP (constant 2011 international $) 2000 9.757370e+11
In [ ]:
df_East_Asia_Pacific_PÏB_par_habitant_courant = df_final_East_Asia_Pacific[df_final_East_Asia_Pacific['Indicator Name'] == 'GDP per capita, PPP (current international $)']
df_East_Asia_Pacific_PÏB_par_habitant_courant.head()
Out[ ]:
Country Name Region Income Group Indicator Name Année value
559 Australia East Asia & Pacific High income: OECD GDP per capita, PPP (current international $) 2000 26406.130951
574 Brunei Darussalam East Asia & Pacific High income: nonOECD GDP per capita, PPP (current international $) 2000 65034.834659
619 Hong Kong SAR, China East Asia & Pacific High income: nonOECD GDP per capita, PPP (current international $) 2000 26962.587770
634 Japan East Asia & Pacific High income: OECD GDP per capita, PPP (current international $) 2000 26795.207427
641 Korea, Rep. East Asia & Pacific High income: OECD GDP per capita, PPP (current international $) 2000 18083.084102
In [ ]:
sns.catplot(x='Année', y='value', data=df_East_Asia_Pacific_PÏB_par_habitant_courant, hue='Country Name', col='Indicator Name', kind='point', height = 10)
Out[ ]:
<seaborn.axisgrid.FacetGrid at 0x127ca4b6490>
In [ ]:
df_East_Asia_Pacific_PIB_par_habitant_constant_2011 = df_final_East_Asia_Pacific[df_final_East_Asia_Pacific['Indicator Name'] == 'GDP per capita, PPP (constant 2011 international $)']
df_East_Asia_Pacific_PIB_par_habitant_constant_2011.head()
Out[ ]:
Country Name Region Income Group Indicator Name Année value
741 Australia East Asia & Pacific High income: OECD GDP per capita, PPP (constant 2011 internation... 2000 35281.395362
758 Brunei Darussalam East Asia & Pacific High income: nonOECD GDP per capita, PPP (constant 2011 internation... 2000 82049.580860
804 Hong Kong SAR, China East Asia & Pacific High income: nonOECD GDP per capita, PPP (constant 2011 internation... 2000 34016.677940
819 Japan East Asia & Pacific High income: OECD GDP per capita, PPP (constant 2011 internation... 2000 33871.843545
826 Korea, Rep. East Asia & Pacific High income: OECD GDP per capita, PPP (constant 2011 internation... 2000 20756.779897
In [ ]:
sns.catplot(x='Année', y='value', data=df_East_Asia_Pacific_PIB_par_habitant_constant_2011, hue='Country Name', col='Indicator Name', kind='point', height = 10)
Out[ ]:
<seaborn.axisgrid.FacetGrid at 0x127981e7dc0>
In [ ]:
df_East_Asia_Pacific_pop_growth = df_final_East_Asia_Pacific[df_final_East_Asia_Pacific['Indicator Name'] == 'Population growth (annual %)']
df_East_Asia_Pacific_pop_growth.head()
Out[ ]:
Country Name Region Income Group Indicator Name Année value
926 Australia East Asia & Pacific High income: OECD Population growth (annual %) 2000 1.192272
942 Brunei Darussalam East Asia & Pacific High income: nonOECD Population growth (annual %) 2000 2.108246
988 Hong Kong SAR, China East Asia & Pacific High income: nonOECD Population growth (annual %) 2000 0.881594
1003 Japan East Asia & Pacific High income: OECD Population growth (annual %) 2000 0.167276
1010 Korea, Rep. East Asia & Pacific High income: OECD Population growth (annual %) 2000 0.836181
In [ ]:
sns.catplot(x='Année', y='value', data=df_East_Asia_Pacific_pop_growth, hue='Country Name', col='Indicator Name', kind='point', height = 10)
Out[ ]:
<seaborn.axisgrid.FacetGrid at 0x127820797c0>
In [ ]:
df_East_Asia_Pacific_pop_totale = df_final_East_Asia_Pacific[df_final_East_Asia_Pacific['Indicator Name'] == 'Population, total']
df_East_Asia_Pacific_pop_totale.head()
Out[ ]:
Country Name Region Income Group Indicator Name Année value
1115 Australia East Asia & Pacific High income: OECD Population, total 2000 19153000.0
1131 Brunei Darussalam East Asia & Pacific High income: nonOECD Population, total 2000 333241.0
1177 Hong Kong SAR, China East Asia & Pacific High income: nonOECD Population, total 2000 6665000.0
1192 Japan East Asia & Pacific High income: OECD Population, total 2000 126843000.0
1199 Korea, Rep. East Asia & Pacific High income: OECD Population, total 2000 47008111.0
In [ ]:
sns.catplot(x='Année', y='value', data=df_East_Asia_Pacific_pop_totale, hue='Country Name', col='Indicator Name', kind='point', height = 10)
Out[ ]:
<seaborn.axisgrid.FacetGrid at 0x127981a3700>
In [ ]:
df_East_Asia_Pacific_Internet_users.describe()
Out[ ]:
value
count 133.000000
mean 63.798312
std 19.421498
min 8.996285
25% 52.689266
50% 69.000000
75% 79.054114
max 92.716545

Analyse statistique

L'écart-type est assez faible, la dispersion est assez proche de la moyenne qui correspond à 63.79 % des utilisateurs d'internet pour la région East Asia / Pacific.

In [ ]:
df5_High_Income_final[df5_High_Income_final['Region'] == 'East Asia & Pacific']
df_final_East_Asia_Pacific = df5_High_Income_final[df5_High_Income_final['Region'] == 'East Asia & Pacific']
df_final_East_Asia_Pacific
Out[ ]:
Country Name Region Income Group Indicator Name Année value
7 Australia East Asia & Pacific High income: OECD Internet users (per 100 people) 2000 46.756116
23 Brunei Darussalam East Asia & Pacific High income: nonOECD Internet users (per 100 people) 2000 8.996285
69 Hong Kong SAR, China East Asia & Pacific High income: nonOECD Internet users (per 100 people) 2000 27.827761
84 Japan East Asia & Pacific High income: OECD Internet users (per 100 people) 2000 29.990740
91 Korea, Rep. East Asia & Pacific High income: OECD Internet users (per 100 people) 2000 44.700000
... ... ... ... ... ... ...
23921 Japan East Asia & Pacific High income: OECD Population, total 2017 NaN
23928 Korea, Rep. East Asia & Pacific High income: OECD Population, total 2017 NaN
23940 Macao SAR, China East Asia & Pacific High income: nonOECD Population, total 2017 NaN
23965 New Zealand East Asia & Pacific High income: OECD Population, total 2017 NaN
23985 Singapore East Asia & Pacific High income: nonOECD Population, total 2017 NaN

1008 rows × 6 columns

Calcul de la moyenne des indicateurs par pays pour la région Asie de l'Est et pacifique

In [ ]:
moy_par_indicateur_East_Asia_Pacific = df_final_East_Asia_Pacific.groupby(['Indicator Name','Country Name'], as_index=False).agg({'value': 'mean'}).sort_values(by = ['Indicator Name','value'], ascending=False)
moy_par_indicateur_East_Asia_Pacific
Out[ ]:
Indicator Name Country Name value
51 Population, total Japan 1.275907e+08
52 Population, total Korea, Rep. 4.910551e+07
48 Population, total Australia 2.145270e+07
50 Population, total Hong Kong SAR, China 6.969818e+06
55 Population, total Singapore 4.781768e+06
54 Population, total New Zealand 4.252300e+06
53 Population, total Macao SAR, China 5.172111e+05
49 Population, total Brunei Darussalam 3.792361e+05
45 Population growth (annual %) Macao SAR, China 2.235763e+00
47 Population growth (annual %) Singapore 2.047911e+00
41 Population growth (annual %) Brunei Darussalam 1.529717e+00
40 Population growth (annual %) Australia 1.428244e+00
46 Population growth (annual %) New Zealand 1.187133e+00
42 Population growth (annual %) Hong Kong SAR, China 6.246895e-01
44 Population growth (annual %) Korea, Rep. 5.569029e-01
43 Population growth (annual %) Japan 1.686188e-02
36 Internet users (per 100 people) Korea, Rep. 7.636007e+01
32 Internet users (per 100 people) Australia 7.275398e+01
38 Internet users (per 100 people) New Zealand 7.200172e+01
35 Internet users (per 100 people) Japan 6.978808e+01
39 Internet users (per 100 people) Singapore 6.484427e+01
34 Internet users (per 100 people) Hong Kong SAR, China 6.354097e+01
37 Internet users (per 100 people) Macao SAR, China 4.834663e+01
33 Internet users (per 100 people) Brunei Darussalam 4.433120e+01
27 GDP, PPP (current international $) Japan 4.332870e+12
28 GDP, PPP (current international $) Korea, Rep. 1.356755e+12
24 GDP, PPP (current international $) Australia 8.105335e+11
31 GDP, PPP (current international $) Singapore 3.147696e+11
26 GDP, PPP (current international $) Hong Kong SAR, China 3.003325e+11
30 GDP, PPP (current international $) New Zealand 1.281219e+11
29 GDP, PPP (current international $) Macao SAR, China 4.384240e+10
25 GDP, PPP (current international $) Brunei Darussalam 2.904290e+10
19 GDP, PPP (constant 2011 international $) Japan 4.567899e+12
20 GDP, PPP (constant 2011 international $) Korea, Rep. 1.390367e+12
16 GDP, PPP (constant 2011 international $) Australia 8.675901e+11
23 GDP, PPP (constant 2011 international $) Singapore 3.252734e+11
18 GDP, PPP (constant 2011 international $) Hong Kong SAR, China 3.126940e+11
22 GDP, PPP (constant 2011 international $) New Zealand 1.376460e+11
21 GDP, PPP (constant 2011 international $) Macao SAR, China 4.467480e+10
17 GDP, PPP (constant 2011 international $) Brunei Darussalam 3.067662e+10
13 GDP per capita, PPP (current international $) Macao SAR, China 8.121553e+04
9 GDP per capita, PPP (current international $) Brunei Darussalam 7.626663e+04
15 GDP per capita, PPP (current international $) Singapore 6.402757e+04
10 GDP per capita, PPP (current international $) Hong Kong SAR, China 4.277564e+04
8 GDP per capita, PPP (current international $) Australia 3.728984e+04
11 GDP per capita, PPP (current international $) Japan 3.395982e+04
14 GDP per capita, PPP (current international $) New Zealand 2.982933e+04
12 GDP per capita, PPP (current international $) Korea, Rep. 2.748882e+04
5 GDP per capita, PPP (constant 2011 internation... Macao SAR, China 8.343582e+04
1 GDP per capita, PPP (constant 2011 internation... Brunei Darussalam 8.108991e+04
7 GDP per capita, PPP (constant 2011 internation... Singapore 6.684773e+04
2 GDP per capita, PPP (constant 2011 internation... Hong Kong SAR, China 4.465806e+04
0 GDP per capita, PPP (constant 2011 internation... Australia 4.024200e+04
3 GDP per capita, PPP (constant 2011 internation... Japan 3.580157e+04
6 GDP per capita, PPP (constant 2011 internation... New Zealand 3.226884e+04
4 GDP per capita, PPP (constant 2011 internation... Korea, Rep. 2.819961e+04

Etude et représentation graphique, par indicateur, pour la région Asie de l'Est et Pacifique

In [ ]:
df_East_Asia_Pacific_Internet_users = df_final_East_Asia_Pacific[df_final_East_Asia_Pacific['Indicator Name'] == 'Internet users (per 100 people)']
df_East_Asia_Pacific_Internet_users.head()
Out[ ]:
Country Name Region Income Group Indicator Name Année value
7 Australia East Asia & Pacific High income: OECD Internet users (per 100 people) 2000 46.756116
23 Brunei Darussalam East Asia & Pacific High income: nonOECD Internet users (per 100 people) 2000 8.996285
69 Hong Kong SAR, China East Asia & Pacific High income: nonOECD Internet users (per 100 people) 2000 27.827761
84 Japan East Asia & Pacific High income: OECD Internet users (per 100 people) 2000 29.990740
91 Korea, Rep. East Asia & Pacific High income: OECD Internet users (per 100 people) 2000 44.700000
In [ ]:
sns.catplot(x='Année', y='value', data=df_East_Asia_Pacific_Internet_users, hue='Country Name', col='Indicator Name', kind='point', height = 10)
Out[ ]:
<seaborn.axisgrid.FacetGrid at 0x127cc162910>

Analyse

La Korée et le japon sont les plus équipés en internet, suivi par la nouvelle-zélande, l'australie et Hong-kong, le tout approchant les 90%.

In [ ]:
df_East_Asia_Pacific_PIB_courant = df_final_East_Asia_Pacific[df_final_East_Asia_Pacific['Indicator Name'] == 'GDP, PPP (current international $)']
df_East_Asia_Pacific_PIB_courant.head()
Out[ ]:
Country Name Region Income Group Indicator Name Année value
192 Australia East Asia & Pacific High income: OECD GDP, PPP (current international $) 2000 5.057566e+11
207 Brunei Darussalam East Asia & Pacific High income: nonOECD GDP, PPP (current international $) 2000 2.167227e+10
252 Hong Kong SAR, China East Asia & Pacific High income: nonOECD GDP, PPP (current international $) 2000 1.797056e+11
267 Japan East Asia & Pacific High income: OECD GDP, PPP (current international $) 2000 3.398784e+12
274 Korea, Rep. East Asia & Pacific High income: OECD GDP, PPP (current international $) 2000 8.500516e+11
In [ ]:
sns.catplot(x='Année', y='value', data=df_East_Asia_Pacific_PIB_courant, hue='Country Name', col='Indicator Name', kind='point', height = 10)
Out[ ]:
<seaborn.axisgrid.FacetGrid at 0x127801d15b0>

Analyse

Le Japon est le pays dégagant le plus de richesse, produisant plus du triple de son suiveur, la Korée.

In [ ]:
df_East_Asia_Pacific_PIB_constant_2011 = df_final_East_Asia_Pacific[df_final_East_Asia_Pacific['Indicator Name'] == 'GDP, PPP (constant 2011 international $)']
df_East_Asia_Pacific_PIB_constant_2011.head()
Out[ ]:
Country Name Region Income Group Indicator Name Année value
374 Australia East Asia & Pacific High income: OECD GDP, PPP (constant 2011 international $) 2000 6.757446e+11
391 Brunei Darussalam East Asia & Pacific High income: nonOECD GDP, PPP (constant 2011 international $) 2000 2.734228e+10
437 Hong Kong SAR, China East Asia & Pacific High income: nonOECD GDP, PPP (constant 2011 international $) 2000 2.267212e+11
452 Japan East Asia & Pacific High income: OECD GDP, PPP (constant 2011 international $) 2000 4.296406e+12
459 Korea, Rep. East Asia & Pacific High income: OECD GDP, PPP (constant 2011 international $) 2000 9.757370e+11
In [ ]:
sns.catplot(x='Année', y='value', data=df_East_Asia_Pacific_PIB_constant_2011, hue='Country Name', col='Indicator Name', kind='point', height = 10)
Out[ ]:
<seaborn.axisgrid.FacetGrid at 0x127cda8be20>
In [ ]:
df_East_Asia_Pacific_PÏB_par_habitant_courant = df_final_East_Asia_Pacific[df_final_East_Asia_Pacific['Indicator Name'] == 'GDP per capita, PPP (current international $)']
df_East_Asia_Pacific_PÏB_par_habitant_courant.head()
Out[ ]:
Country Name Region Income Group Indicator Name Année value
559 Australia East Asia & Pacific High income: OECD GDP per capita, PPP (current international $) 2000 26406.130951
574 Brunei Darussalam East Asia & Pacific High income: nonOECD GDP per capita, PPP (current international $) 2000 65034.834659
619 Hong Kong SAR, China East Asia & Pacific High income: nonOECD GDP per capita, PPP (current international $) 2000 26962.587770
634 Japan East Asia & Pacific High income: OECD GDP per capita, PPP (current international $) 2000 26795.207427
641 Korea, Rep. East Asia & Pacific High income: OECD GDP per capita, PPP (current international $) 2000 18083.084102
In [ ]:
sns.catplot(x='Année', y='value', data=df_East_Asia_Pacific_PÏB_par_habitant_courant, hue='Country Name', col='Indicator Name', kind='point', height = 10)
Out[ ]:
<seaborn.axisgrid.FacetGrid at 0x127da75ee20>

Analyse

Macao, une région administrative de la Chine (elle constitue donc un segment distinct sur le plan statistique) possède le niveau de richesse par habitant le plus élevé (mais a nettement baissé entre 2013 et 2017), suivi par Singapour et Brunei Darussalam. Ce dernier est un micro-état et il est peu probable qu'il constitue un pays d'implantation intéressant.

In [ ]:
df_East_Asia_Pacific_PIB_par_habitant_constant_2011 = df_final_East_Asia_Pacific[df_final_East_Asia_Pacific['Indicator Name'] == 'GDP per capita, PPP (constant 2011 international $)']
df_East_Asia_Pacific_PIB_par_habitant_constant_2011.head()
Out[ ]:
Country Name Region Income Group Indicator Name Année value
741 Australia East Asia & Pacific High income: OECD GDP per capita, PPP (constant 2011 internation... 2000 35281.395362
758 Brunei Darussalam East Asia & Pacific High income: nonOECD GDP per capita, PPP (constant 2011 internation... 2000 82049.580860
804 Hong Kong SAR, China East Asia & Pacific High income: nonOECD GDP per capita, PPP (constant 2011 internation... 2000 34016.677940
819 Japan East Asia & Pacific High income: OECD GDP per capita, PPP (constant 2011 internation... 2000 33871.843545
826 Korea, Rep. East Asia & Pacific High income: OECD GDP per capita, PPP (constant 2011 internation... 2000 20756.779897
In [ ]:
sns.catplot(x='Année', y='value', data=df_East_Asia_Pacific_PIB_par_habitant_constant_2011, hue='Country Name', col='Indicator Name', kind='point', height = 10)
Out[ ]:
<seaborn.axisgrid.FacetGrid at 0x127da77bfd0>
In [ ]:
df_East_Asia_Pacific_pop_growth = df_final_East_Asia_Pacific[df_final_East_Asia_Pacific['Indicator Name'] == 'Population growth (annual %)']
df_East_Asia_Pacific_pop_growth.head()
Out[ ]:
Country Name Region Income Group Indicator Name Année value
926 Australia East Asia & Pacific High income: OECD Population growth (annual %) 2000 1.192272
942 Brunei Darussalam East Asia & Pacific High income: nonOECD Population growth (annual %) 2000 2.108246
988 Hong Kong SAR, China East Asia & Pacific High income: nonOECD Population growth (annual %) 2000 0.881594
1003 Japan East Asia & Pacific High income: OECD Population growth (annual %) 2000 0.167276
1010 Korea, Rep. East Asia & Pacific High income: OECD Population growth (annual %) 2000 0.836181
In [ ]:
sns.catplot(x='Année', y='value', data=df_East_Asia_Pacific_pop_growth, hue='Country Name', col='Indicator Name', kind='point', height = 10)
Out[ ]:
<seaborn.axisgrid.FacetGrid at 0x127da759c10>
In [ ]:
df_East_Asia_Pacific_pop_totale = df_final_East_Asia_Pacific[df_final_East_Asia_Pacific['Indicator Name'] == 'Population, total']
df_East_Asia_Pacific_pop_totale.head()
Out[ ]:
Country Name Region Income Group Indicator Name Année value
1115 Australia East Asia & Pacific High income: OECD Population, total 2000 19153000.0
1131 Brunei Darussalam East Asia & Pacific High income: nonOECD Population, total 2000 333241.0
1177 Hong Kong SAR, China East Asia & Pacific High income: nonOECD Population, total 2000 6665000.0
1192 Japan East Asia & Pacific High income: OECD Population, total 2000 126843000.0
1199 Korea, Rep. East Asia & Pacific High income: OECD Population, total 2000 47008111.0
In [ ]:
sns.catplot(x='Année', y='value', data=df_East_Asia_Pacific_pop_totale, hue='Country Name', col='Indicator Name', kind='point', height = 10)
Out[ ]:
<seaborn.axisgrid.FacetGrid at 0x1279c86cc70>

Analyse

Le Japon semble ici comporter la population la plus importante de la région Asie de l'Est et Pacifique, toutefois cela semble très peu probable; du fait de l'absence d'autres pays majeut (comme la Chine) dans la représentation graphique. Sans la présence de ces données il semble difficile de prendre du recul sur le niveau élevé de richesse par habitant de Macao (Chine). Si nous la retenons pas dnans ce classement, le Japon et la Korée sont les meilleurs cibles.

Indicateurs relatifs à l'éducation

In [ ]:
df5_melted_High_Income['Indicator Name'].unique().tolist()
Out[ ]:
['Internet users (per 100 people)',
 'GDP, PPP (current international $)',
 'GDP, PPP (constant 2011 international $)',
 'GDP per capita, PPP (current international $)',
 'GDP per capita, PPP (constant 2011 international $)',
 'Population growth (annual %)',
 'Population, total',
 'Government expenditure on secondary education as % of GDP (%)',
 'Government expenditure on tertiary education as % of GDP (%)']
In [ ]:
Educ_East_Asia_Pacific = df5_melted_High_Income[df5_melted_High_Income['Region'] == 'East Asia & Pacific']
Educ_East_Asia_Pacific
Out[ ]:
Country Name Region Income Group Indicator Name Année value
7 Australia East Asia & Pacific High income: OECD Internet users (per 100 people) 2000 46.756116
23 Brunei Darussalam East Asia & Pacific High income: nonOECD Internet users (per 100 people) 2000 8.996285
69 Hong Kong SAR, China East Asia & Pacific High income: nonOECD Internet users (per 100 people) 2000 27.827761
84 Japan East Asia & Pacific High income: OECD Internet users (per 100 people) 2000 29.990740
91 Korea, Rep. East Asia & Pacific High income: OECD Internet users (per 100 people) 2000 44.700000
... ... ... ... ... ... ...
23921 Japan East Asia & Pacific High income: OECD Population, total 2017 NaN
23928 Korea, Rep. East Asia & Pacific High income: OECD Population, total 2017 NaN
23940 Macao SAR, China East Asia & Pacific High income: nonOECD Population, total 2017 NaN
23965 New Zealand East Asia & Pacific High income: OECD Population, total 2017 NaN
23985 Singapore East Asia & Pacific High income: nonOECD Population, total 2017 NaN

1008 rows × 6 columns

In [ ]:
Education_East_Asia_and_Pacific_2dary = Educ_East_Asia_Pacific[Educ_East_Asia_Pacific['Indicator Name'] == 'Government expenditure per secondary student as % of GDP per capita (%)']
Education_East_Asia_and_Pacific_2dary
Out[ ]:
Country Name Region Income Group Indicator Name Année value
Il n'y a aucune données pour le secteur secondaire
In [ ]:
Education_East_Asia_and_Pacific_3tiary = Educ_East_Asia_Pacific[Educ_East_Asia_Pacific['Indicator Name'] == 'Government expenditure per tertiary student as % of GDP per capita (%)']
sns.catplot(x='Année', y='value', data=Education_East_Asia_and_Pacific_3tiary, hue='Country Name', kind='point', height = 10)Il n'y a aucune données pour le secteur supérieurNous allons maintenant étudier les autres régions intéressantes : L'Amérique du Nord L'Europe et l'Asie Centrale La région Nord Afrique et Centre Est

Nord Afrique et Centre Est

In [ ]:
df_Middle_East_and_North_Africa = df5_melted_High_Income[df5_melted_High_Income['Region'] =='Middle East & North Africa']
df_Middle_East_and_North_Africa
Out[ ]:
Country Name Region Income Group Indicator Name Année value
3 United Arab Emirates Middle East & North Africa High income: nonOECD Internet users (per 100 people) 2000 23.625301
16 Bahrain Middle East & North Africa High income: nonOECD Internet users (per 100 people) 2000 6.153733
80 Israel Middle East & North Africa High income: OECD Internet users (per 100 people) 2000 20.873790
92 Kuwait Middle East & North Africa High income: nonOECD Internet users (per 100 people) 2000 6.731396
112 Malta Middle East & North Africa High income: nonOECD Internet users (per 100 people) 2000 13.113709
... ... ... ... ... ... ...
23929 Kuwait Middle East & North Africa High income: nonOECD Population, total 2017 NaN
23949 Malta Middle East & North Africa High income: nonOECD Population, total 2017 NaN
23966 Oman Middle East & North Africa High income: nonOECD Population, total 2017 NaN
23978 Qatar Middle East & North Africa High income: nonOECD Population, total 2017 NaN
23982 Saudi Arabia Middle East & North Africa High income: nonOECD Population, total 2017 NaN

972 rows × 6 columns

Calcul de la moyenne pour la région Afrique du Nord et Centre Est

In [ ]:
moy_par_indicateur_Middle_East_and_North_Africa = df_Middle_East_and_North_Africa.groupby(['Indicator Name','Country Name'], as_index=False).agg({'value': 'mean'}).sort_values(by = ['Indicator Name','value'], ascending=False)
moy_par_indicateur_Middle_East_and_North_Africa
Out[ ]:
Indicator Name Country Name value
52 Population, total Saudi Arabia 2.620014e+07
47 Population, total Israel 7.368076e+06
53 Population, total United Arab Emirates 6.505238e+06
50 Population, total Oman 3.009788e+06
48 Population, total Kuwait 2.839027e+06
51 Population, total Qatar 1.462634e+06
46 Population, total Bahrain 1.068067e+06
49 Population, total Malta 4.109674e+05
43 Population growth (annual %) Qatar 8.864231e+00
45 Population growth (annual %) United Arab Emirates 6.659312e+00
38 Population growth (annual %) Bahrain 4.741129e+00
40 Population growth (annual %) Kuwait 4.281813e+00
42 Population growth (annual %) Oman 3.965311e+00
44 Population growth (annual %) Saudi Arabia 2.729229e+00
39 Population growth (annual %) Israel 1.960078e+00
41 Population growth (annual %) Malta 7.052635e-01
37 Internet users (per 100 people) United Arab Emirates 5.931311e+01
30 Internet users (per 100 people) Bahrain 5.068505e+01
33 Internet users (per 100 people) Malta 5.049638e+01
31 Internet users (per 100 people) Israel 4.892158e+01
35 Internet users (per 100 people) Qatar 4.826927e+01
32 Internet users (per 100 people) Kuwait 4.475584e+01
36 Internet users (per 100 people) Saudi Arabia 3.404459e+01
34 Internet users (per 100 people) Oman 3.089485e+01
28 GDP, PPP (current international $) Saudi Arabia 1.169914e+12
29 GDP, PPP (current international $) United Arab Emirates 4.512623e+11
23 GDP, PPP (current international $) Israel 2.158391e+11
24 GDP, PPP (current international $) Kuwait 2.141406e+11
27 GDP, PPP (current international $) Qatar 1.731165e+11
26 GDP, PPP (current international $) Oman 1.187612e+11
25 GDP, PPP (current international $) Malta 1.094762e+10
21 GDP, PPP (constant 2011 international $) Saudi Arabia 1.216317e+12
22 GDP, PPP (constant 2011 international $) United Arab Emirates 4.694754e+11
17 GDP, PPP (constant 2011 international $) Kuwait 2.227807e+11
16 GDP, PPP (constant 2011 international $) Israel 2.138769e+11
20 GDP, PPP (constant 2011 international $) Qatar 1.751089e+11
19 GDP, PPP (constant 2011 international $) Oman 1.249886e+11
15 GDP, PPP (constant 2011 international $) Bahrain 4.441551e+10
18 GDP, PPP (constant 2011 international $) Malta 1.165752e+10
12 GDP per capita, PPP (current international $) Qatar 1.112642e+05
9 GDP per capita, PPP (current international $) Kuwait 7.483535e+04
14 GDP per capita, PPP (current international $) United Arab Emirates 7.215048e+04
13 GDP per capita, PPP (current international $) Saudi Arabia 4.369104e+04
11 GDP per capita, PPP (current international $) Oman 4.011984e+04
8 GDP per capita, PPP (current international $) Israel 2.890720e+04
10 GDP per capita, PPP (current international $) Malta 2.645934e+04
5 GDP per capita, PPP (constant 2011 internation... Qatar 1.174306e+05
2 GDP per capita, PPP (constant 2011 internation... Kuwait 7.944382e+04
7 GDP per capita, PPP (constant 2011 internation... United Arab Emirates 7.791499e+04
6 GDP per capita, PPP (constant 2011 internation... Saudi Arabia 4.600664e+04
4 GDP per capita, PPP (constant 2011 internation... Oman 4.296698e+04
0 GDP per capita, PPP (constant 2011 internation... Bahrain 4.263905e+04
1 GDP per capita, PPP (constant 2011 internation... Israel 2.882084e+04
3 GDP per capita, PPP (constant 2011 internation... Malta 2.826661e+04
In [ ]:
df_Middle_East_and_North_Africa_Internet_users = df_Middle_East_and_North_Africa[df_Middle_East_and_North_Africa['Indicator Name'] == 'Internet users (per 100 people)']
df_Middle_East_and_North_Africa_Internet_users.head()
Out[ ]:
Country Name Region Income Group Indicator Name Année value
3 United Arab Emirates Middle East & North Africa High income: nonOECD Internet users (per 100 people) 2000 23.625301
16 Bahrain Middle East & North Africa High income: nonOECD Internet users (per 100 people) 2000 6.153733
80 Israel Middle East & North Africa High income: OECD Internet users (per 100 people) 2000 20.873790
92 Kuwait Middle East & North Africa High income: nonOECD Internet users (per 100 people) 2000 6.731396
112 Malta Middle East & North Africa High income: nonOECD Internet users (per 100 people) 2000 13.113709
In [ ]:
sns.catplot(x='Année', y='value', data=df_Middle_East_and_North_Africa_Internet_users, hue='Country Name', col='Indicator Name', kind='point', height = 10)
Out[ ]:
<seaborn.axisgrid.FacetGrid at 0x1283657dbb0>

Analyse

On constate que 3 pays se distingue nettement : Bahrain , les Emirats Arabes Unis et le Qatar sont équipés à + de 95% d'internet en 2017.

In [ ]:
df_Middle_East_and_North_Africa_PIB_courant = df_Middle_East_and_North_Africa[df_Middle_East_and_North_Africa['Indicator Name'] == 'GDP, PPP (current international $)']
df_Middle_East_and_North_Africa_PIB_courant.head()
Out[ ]:
Country Name Region Income Group Indicator Name Année value
188 United Arab Emirates Middle East & North Africa High income: nonOECD GDP, PPP (current international $) 2000 2.581316e+11
263 Israel Middle East & North Africa High income: OECD GDP, PPP (current international $) 2000 1.567918e+11
275 Kuwait Middle East & North Africa High income: nonOECD GDP, PPP (current international $) 2000 1.136535e+11
294 Malta Middle East & North Africa High income: nonOECD GDP, PPP (current international $) 2000 7.571892e+09
311 Oman Middle East & North Africa High income: nonOECD GDP, PPP (current international $) 2000 7.903575e+10
In [ ]:
sns.catplot(x='Année', y='value', data=df_Middle_East_and_North_Africa_PIB_courant, hue='Country Name', col='Indicator Name', kind='point', height = 10)
Out[ ]:
<seaborn.axisgrid.FacetGrid at 0x12836ca6850>

Analyse

Le PIB de l'arabie saoudite dépasse de loin celui des autres pays, le deuxième étant les émirats arabes unis.

In [ ]:
df_Middle_East_and_North_Africa_PIB_constant_2011 = df_Middle_East_and_North_Africa[df_Middle_East_and_North_Africa['Indicator Name'] == 'GDP, PPP (constant 2011 international $)']
df_Middle_East_and_North_Africa_PIB_constant_2011.head()
Out[ ]:
Country Name Region Income Group Indicator Name Année value
370 United Arab Emirates Middle East & North Africa High income: nonOECD GDP, PPP (constant 2011 international $) 2000 3.256653e+11
383 Bahrain Middle East & North Africa High income: nonOECD GDP, PPP (constant 2011 international $) 2000 2.986933e+10
448 Israel Middle East & North Africa High income: OECD GDP, PPP (constant 2011 international $) 2000 1.687006e+11
460 Kuwait Middle East & North Africa High income: nonOECD GDP, PPP (constant 2011 international $) 2000 1.433882e+11
479 Malta Middle East & North Africa High income: nonOECD GDP, PPP (constant 2011 international $) 2000 9.593600e+09
In [ ]:
sns.catplot(x='Année', y='value', data=df_Middle_East_and_North_Africa_PIB_constant_2011, hue='Country Name', col='Indicator Name', kind='point', height = 10)
Out[ ]:
<seaborn.axisgrid.FacetGrid at 0x127981a3e80>
In [ ]:
df_Middle_East_and_North_Africa_PÏB_par_habitant_courant = df_Middle_East_and_North_Africa[df_Middle_East_and_North_Africa['Indicator Name'] == 'GDP per capita, PPP (current international $)']
df_Middle_East_and_North_Africa_PÏB_par_habitant_courant.head()
Out[ ]:
Country Name Region Income Group Indicator Name Année value
555 United Arab Emirates Middle East & North Africa High income: nonOECD GDP per capita, PPP (current international $) 2000 81818.620517
630 Israel Middle East & North Africa High income: OECD GDP per capita, PPP (current international $) 2000 24931.111060
642 Kuwait Middle East & North Africa High income: nonOECD GDP per capita, PPP (current international $) 2000 55420.722652
661 Malta Middle East & North Africa High income: nonOECD GDP per capita, PPP (current international $) 2000 19410.776349
678 Oman Middle East & North Africa High income: nonOECD GDP per capita, PPP (current international $) 2000 34848.351287
In [ ]:
sns.catplot(x='Année', y='value', data=df_Middle_East_and_North_Africa_PÏB_par_habitant_courant, hue='Country Name', col='Indicator Name', kind='point', height = 10)
Out[ ]:
<seaborn.axisgrid.FacetGrid at 0x128375e16d0>

Analyse

Le Qatar est le pays le plus riche par habitant, suivi de loin par le koweit et les émirats arabes unis.

In [ ]:
df_Middle_East_and_North_Africa_PIB_par_habitant_constant_2011 = df_Middle_East_and_North_Africa[df_Middle_East_and_North_Africa['Indicator Name'] == 'GDP per capita, PPP (constant 2011 international $)']
df_Middle_East_and_North_Africa_PIB_par_habitant_constant_2011.head()
Out[ ]:
Country Name Region Income Group Indicator Name Année value
737 United Arab Emirates Middle East & North Africa High income: nonOECD GDP per capita, PPP (constant 2011 internation... 2000 103224.426650
750 Bahrain Middle East & North Africa High income: nonOECD GDP per capita, PPP (constant 2011 internation... 2000 44942.369023
815 Israel Middle East & North Africa High income: OECD GDP per capita, PPP (constant 2011 internation... 2000 26824.705115
827 Kuwait Middle East & North Africa High income: nonOECD GDP per capita, PPP (constant 2011 internation... 2000 69920.175678
846 Malta Middle East & North Africa High income: nonOECD GDP per capita, PPP (constant 2011 internation... 2000 24593.487674
In [ ]:
sns.catplot(x='Année', y='value', data=df_Middle_East_and_North_Africa_PIB_par_habitant_constant_2011, hue='Country Name', col='Indicator Name', kind='point', height = 10)
Out[ ]:
<seaborn.axisgrid.FacetGrid at 0x1285efb3280>
In [ ]:
df_Middle_East_and_North_Africa_pop_growth = df_Middle_East_and_North_Africa[df_Middle_East_and_North_Africa['Indicator Name'] == 'Population growth (annual %)']
df_Middle_East_and_North_Africa_pop_growth.head()
Out[ ]:
Country Name Region Income Group Indicator Name Année value
922 United Arab Emirates Middle East & North Africa High income: nonOECD Population growth (annual %) 2000 5.430624
935 Bahrain Middle East & North Africa High income: nonOECD Population growth (annual %) 2000 4.315131
999 Israel Middle East & North Africa High income: OECD Population growth (annual %) 2000 2.642332
1011 Kuwait Middle East & North Africa High income: nonOECD Population growth (annual %) 2000 4.675478
1031 Malta Middle East & North Africa High income: nonOECD Population growth (annual %) 2000 0.645267
In [ ]:
sns.catplot(x='Année', y='value', data=df_Middle_East_and_North_Africa_pop_growth, hue='Country Name', col='Indicator Name', kind='point', height = 10)
Out[ ]:
<seaborn.axisgrid.FacetGrid at 0x1285f6b4610>

Analyse

Le qatar et les émirats arabes unis connaissent un ralentissement de la croissance de leur population.

In [ ]:
df_Middle_East_and_North_Africa_pop_totale = df_Middle_East_and_North_Africa[df_Middle_East_and_North_Africa['Indicator Name'] == 'Population, total']
df_Middle_East_and_North_Africa_pop_totale.head()
Out[ ]:
Country Name Region Income Group Indicator Name Année value
1111 United Arab Emirates Middle East & North Africa High income: nonOECD Population, total 2000 3154925.0
1124 Bahrain Middle East & North Africa High income: nonOECD Population, total 2000 664614.0
1188 Israel Middle East & North Africa High income: OECD Population, total 2000 6289000.0
1200 Kuwait Middle East & North Africa High income: nonOECD Population, total 2000 2050741.0
1220 Malta Middle East & North Africa High income: nonOECD Population, total 2000 390087.0
In [ ]:
sns.catplot(x='Année', y='value', data=df_Middle_East_and_North_Africa_pop_totale, hue='Country Name', col='Indicator Name', kind='point', height = 10)
Out[ ]:
<seaborn.axisgrid.FacetGrid at 0x1286040f610>

Analyse

L'arabie saoudite possède de très loin la population la plus importante devant les émirats arabes et israël.

Indicateurs relatifs à l'éducation

In [ ]:
df_Middle_East_and_North_Africa[df_Middle_East_and_North_Africa['Indicator Name'] == 'Government expenditure per secondary student as % of GDP per capita (%)']
Out[ ]:
Country Name Region Income Group Indicator Name Année value
Il n'y a pas de données pour cette variable
In [ ]:
df_Middle_East_and_North_Africa[df_Middle_East_and_North_Africa['Indicator Name'] == 'Government expenditure per tertiary student as % of GDP per capita (%)']
Out[ ]:
Country Name Region Income Group Indicator Name Année value
Il n'y a pas de données pour cette variable

Amérique du Nord

In [ ]:
df_North_America = df5_melted_High_Income[df5_melted_High_Income['Region'] =='North America']
df_North_America
Out[ ]:
Country Name Region Income Group Indicator Name Année value
27 Canada North America High income: OECD Internet users (per 100 people) 2000 5.130000e+01
174 United States North America High income: OECD Internet users (per 100 people) 2000 4.307916e+01
211 Canada North America High income: OECD GDP, PPP (current international $) 2000 8.980246e+11
355 United States North America High income: OECD GDP, PPP (current international $) 2000 1.028478e+13
395 Canada North America High income: OECD GDP, PPP (constant 2011 international $) 2000 1.151769e+12
... ... ... ... ... ... ...
23636 United States North America High income: OECD GDP per capita, PPP (constant 2011 internation... 2017 NaN
23675 Canada North America High income: OECD Population growth (annual %) 2017 NaN
23825 United States North America High income: OECD Population growth (annual %) 2017 NaN
23864 Canada North America High income: OECD Population, total 2017 NaN
24014 United States North America High income: OECD Population, total 2017 NaN

252 rows × 6 columns

In [ ]:
moy_par_indicateur_North_America = df_North_America.groupby(['Indicator Name','Country Name'], as_index=False).agg({'value': 'mean'}).sort_values(by = ['Indicator Name','value'], ascending=False)
moy_par_indicateur_North_America
Out[ ]:
Indicator Name Country Name value
13 Population, total United States 3.033803e+08
12 Population, total Canada 3.338018e+07
10 Population growth (annual %) Canada 1.022019e+00
11 Population growth (annual %) United States 8.628934e-01
8 Internet users (per 100 people) Canada 7.500231e+01
9 Internet users (per 100 people) United States 6.738532e+01
7 GDP, PPP (current international $) United States 1.433539e+13
6 GDP, PPP (current international $) Canada 1.284371e+12
5 GDP, PPP (constant 2011 international $) United States 1.507389e+13
4 GDP, PPP (constant 2011 international $) Canada 1.362130e+12
3 GDP per capita, PPP (current international $) United States 4.698856e+04
2 GDP per capita, PPP (current international $) Canada 3.822688e+04
1 GDP per capita, PPP (constant 2011 internation... United States 4.960544e+04
0 GDP per capita, PPP (constant 2011 internation... Canada 4.072493e+04
In [ ]:
df_North_America_Internet_users = df_North_America[df_North_America['Indicator Name'] == 'Internet users (per 100 people)']
df_North_America_Internet_users.head()
Out[ ]:
Country Name Region Income Group Indicator Name Année value
27 Canada North America High income: OECD Internet users (per 100 people) 2000 51.300000
174 United States North America High income: OECD Internet users (per 100 people) 2000 43.079163
1364 Canada North America High income: OECD Internet users (per 100 people) 2001 60.200000
1511 United States North America High income: OECD Internet users (per 100 people) 2001 49.080832
2701 Canada North America High income: OECD Internet users (per 100 people) 2002 61.593299
In [ ]:
sns.catplot(x='Année', y='value', data=df_North_America_Internet_users, hue='Country Name', col='Indicator Name', kind='point', height = 10)
Out[ ]:
<seaborn.axisgrid.FacetGrid at 0x12860b1e4c0>

Analyse

Le canada est dôté à plus de 90% d'internet et les Etats unis à plus de 70%.

In [ ]:
df_North_America_PIB_courant = df_North_America[df_North_America['Indicator Name'] == 'GDP, PPP (current international $)']
df_North_America_PIB_courant.head()
Out[ ]:
Country Name Region Income Group Indicator Name Année value
211 Canada North America High income: OECD GDP, PPP (current international $) 2000 8.980246e+11
355 United States North America High income: OECD GDP, PPP (current international $) 2000 1.028478e+13
1548 Canada North America High income: OECD GDP, PPP (current international $) 2001 9.347544e+11
1692 United States North America High income: OECD GDP, PPP (current international $) 2001 1.062182e+13
2885 Canada North America High income: OECD GDP, PPP (current international $) 2002 9.675588e+11
In [ ]:
sns.catplot(x='Année', y='value', data=df_North_America_PIB_courant, hue='Country Name', col='Indicator Name', kind='point', height = 10)
Out[ ]:
<seaborn.axisgrid.FacetGrid at 0x12860406c10>
In [ ]:
df_North_America_PIB_constant_2011 = df_North_America[df_North_America['Indicator Name'] == 'GDP, PPP (constant 2011 international $)']
df_North_America_PIB_constant_2011.head()
Out[ ]:
Country Name Region Income Group Indicator Name Année value
395 Canada North America High income: OECD GDP, PPP (constant 2011 international $) 2000 1.151769e+12
540 United States North America High income: OECD GDP, PPP (constant 2011 international $) 2000 1.297554e+13
1732 Canada North America High income: OECD GDP, PPP (constant 2011 international $) 2001 1.172165e+12
1877 United States North America High income: OECD GDP, PPP (constant 2011 international $) 2001 1.310217e+13
3069 Canada North America High income: OECD GDP, PPP (constant 2011 international $) 2002 1.207447e+12
In [ ]:
sns.catplot(x='Année', y='value', data=df_North_America_PIB_constant_2011, hue='Country Name', col='Indicator Name', kind='point', height = 10)
Out[ ]:
<seaborn.axisgrid.FacetGrid at 0x1286040f400>

Analyse

Le canada ne dégage que très peu de richesse de sa production contrairement aux Etats-Unis.

In [ ]:
df_North_America_PÏB_par_habitant_courant = df_North_America[df_North_America['Indicator Name'] == 'GDP per capita, PPP (current international $)']
df_North_America_PÏB_par_habitant_courant.head()
Out[ ]:
Country Name Region Income Group Indicator Name Année value
578 Canada North America High income: OECD GDP per capita, PPP (current international $) 2000 29185.355059
722 United States North America High income: OECD GDP per capita, PPP (current international $) 2000 36449.855116
1915 Canada North America High income: OECD GDP per capita, PPP (current international $) 2001 30073.912805
2059 United States North America High income: OECD GDP per capita, PPP (current international $) 2001 37273.618103
3252 Canada North America High income: OECD GDP per capita, PPP (current international $) 2002 30851.311482
In [ ]:
sns.catplot(x='Année', y='value', data=df_North_America_PÏB_par_habitant_courant, hue='Country Name', col='Indicator Name', kind='point', height = 10)
Out[ ]:
<seaborn.axisgrid.FacetGrid at 0x128616c1f10>
In [ ]:
df_North_America_PIB_par_habitant_constant_2011 = df_North_America[df_North_America['Indicator Name'] == 'GDP per capita, PPP (constant 2011 international $)']
df_North_America_PIB_par_habitant_constant_2011.head()
Out[ ]:
Country Name Region Income Group Indicator Name Année value
762 Canada North America High income: OECD GDP per capita, PPP (constant 2011 internation... 2000 37431.916978
907 United States North America High income: OECD GDP per capita, PPP (constant 2011 internation... 2000 45986.052710
2099 Canada North America High income: OECD GDP per capita, PPP (constant 2011 internation... 2001 37712.128235
2244 United States North America High income: OECD GDP per capita, PPP (constant 2011 internation... 2001 45977.549991
3436 Canada North America High income: OECD GDP per capita, PPP (constant 2011 internation... 2002 38500.317077
In [ ]:
sns.catplot(x='Année', y='value', data=df_North_America_PIB_par_habitant_constant_2011, hue='Country Name', col='Indicator Name', kind='point', height = 10)
Out[ ]:
<seaborn.axisgrid.FacetGrid at 0x12861c353d0>

Analyse

Le niveau de richesse par habitant des Etats-Unis est bien plus important qu'au Canada, le rendant plus attractif.

In [ ]:
df_North_America_pop_growth = df_North_America[df_North_America['Indicator Name'] == 'Population growth (annual %)']
df_North_America_pop_growth.head()
Out[ ]:
Country Name Region Income Group Indicator Name Année value
946 Canada North America High income: OECD Population growth (annual %) 2000 0.882999
1096 United States North America High income: OECD Population growth (annual %) 2000 1.112769
2283 Canada North America High income: OECD Population growth (annual %) 2001 1.009522
2433 United States North America High income: OECD Population growth (annual %) 2001 0.989741
3620 Canada North America High income: OECD Population growth (annual %) 2002 0.897131
In [ ]:
sns.catplot(x='Année', y='value', data=df_North_America_pop_growth, hue='Country Name', col='Indicator Name', kind='point', height = 10)
Out[ ]:
<seaborn.axisgrid.FacetGrid at 0x1286211f9d0>
In [ ]:
df_North_America_pop_totale = df_North_America[df_North_America['Indicator Name'] == 'Population, total']
df_North_America_pop_totale.head()
Out[ ]:
Country Name Region Income Group Indicator Name Année value
1135 Canada North America High income: OECD Population, total 2000 30769700.0
1285 United States North America High income: OECD Population, total 2000 282162411.0
2472 Canada North America High income: OECD Population, total 2001 31081900.0
2622 United States North America High income: OECD Population, total 2001 284968955.0
3809 Canada North America High income: OECD Population, total 2002 31362000.0
In [ ]:
sns.catplot(x='Année', y='value', data=df_North_America_pop_totale, hue='Country Name', col='Indicator Name', kind='point', height = 10)
Out[ ]:
<seaborn.axisgrid.FacetGrid at 0x128626c6730>

Analyse

Les Etats-Unis comporte de très loin la population la plus importante, avec un PIB par habitant extrêmement élevé. Les perspective de profit y sont importantes.

Indicateurs relatifs à l'éducation

In [ ]:
df_North_America[df_North_America['Indicator Name'] == 'Government expenditure per secondary student as % of GDP per capita (%)']
Out[ ]:
Country Name Region Income Group Indicator Name Année value
aucune données
In [ ]:
df_North_America[df_North_America['Indicator Name'] == 'Government expenditure per tertiary student as % of GDP per capita (%)']
Out[ ]:
Country Name Region Income Group Indicator Name Année value

Europe et Asie centrale

In [ ]:
df_Europe_et_Asie_centrale = df5_melted_High_Income[df5_melted_High_Income['Region'] == 'Europe & Central Asia']
df_Europe_et_Asie_centrale
Out[ ]:
Country Name Region Income Group Indicator Name Année value
8 Austria Europe & Central Asia High income: OECD Internet users (per 100 people) 2000 33.730133
11 Belgium Europe & Central Asia High income: OECD Internet users (per 100 people) 2000 29.431692
28 Switzerland Europe & Central Asia High income: OECD Internet users (per 100 people) 2000 47.100000
40 Cyprus Europe & Central Asia High income: nonOECD Internet users (per 100 people) 2000 15.255394
41 Czech Republic Europe & Central Asia High income: OECD Internet users (per 100 people) 2000 9.780528
... ... ... ... ... ... ...
23995 Sweden Europe & Central Asia High income: OECD Population, total 2017 NaN
24037 Latvia Europe & Central Asia High income: nonOECD Government expenditure on secondary education ... 2017 NaN
24042 Slovak Republic Europe & Central Asia High income: OECD Government expenditure on secondary education ... 2017 NaN
24057 Latvia Europe & Central Asia High income: nonOECD Government expenditure on tertiary education a... 2017 NaN
24061 Slovak Republic Europe & Central Asia High income: OECD Government expenditure on tertiary education a... 2017 NaN

3600 rows × 6 columns

In [ ]:
moy_par_indicateur_Europe_et_Asie_centrale = df_Europe_et_Asie_centrale.groupby(['Indicator Name','Country Name'], as_index=False).agg({'value': 'mean'}).sort_values(by = ['Indicator Name','value'], ascending=False)
moy_par_indicateur_Europe_et_Asie_centrale
Out[ ]:
Indicator Name Country Name value
193 Population, total Russian Federation 1.438984e+08
181 Population, total Germany 8.186383e+07
180 Population, total France 6.416499e+07
199 Population, total United Kingdom 6.193559e+07
185 Population, total Italy 5.876462e+07
... ... ... ...
21 GDP per capita, PPP (constant 2011 internation... Russian Federation 2.110942e+04
15 GDP per capita, PPP (constant 2011 internation... Lithuania 2.068096e+04
19 GDP per capita, PPP (constant 2011 internation... Poland 2.004343e+04
2 GDP per capita, PPP (constant 2011 internation... Croatia 1.962578e+04
14 GDP per capita, PPP (constant 2011 internation... Latvia 1.852342e+04

200 rows × 3 columns

Représentation graphique

Nous allons maintenant étudier une par une les différentes variables

In [ ]:
df_Europe_et_Asie_centrale['Indicator Name'].unique().tolist()
Out[ ]:
['Internet users (per 100 people)',
 'GDP, PPP (current international $)',
 'GDP, PPP (constant 2011 international $)',
 'GDP per capita, PPP (current international $)',
 'GDP per capita, PPP (constant 2011 international $)',
 'Population growth (annual %)',
 'Population, total',
 'Government expenditure on secondary education as % of GDP (%)',
 'Government expenditure on tertiary education as % of GDP (%)']
In [ ]:
df_Europe_et_Asie_centrale_Internet_users = df_Europe_et_Asie_centrale[df_Europe_et_Asie_centrale['Indicator Name'] == 'Internet users (per 100 people)']
df_Europe_et_Asie_centrale_Internet_users.head()
Out[ ]:
Country Name Region Income Group Indicator Name Année value
8 Austria Europe & Central Asia High income: OECD Internet users (per 100 people) 2000 33.730133
11 Belgium Europe & Central Asia High income: OECD Internet users (per 100 people) 2000 29.431692
28 Switzerland Europe & Central Asia High income: OECD Internet users (per 100 people) 2000 47.100000
40 Cyprus Europe & Central Asia High income: nonOECD Internet users (per 100 people) 2000 15.255394
41 Czech Republic Europe & Central Asia High income: OECD Internet users (per 100 people) 2000 9.780528
In [ ]:
sns.catplot(x='Année', y='value', data=df_Europe_et_Asie_centrale_Internet_users, hue='Country Name', col='Indicator Name', kind='point', height = 10)
Out[ ]:
<seaborn.axisgrid.FacetGrid at 0x12862bbc460>

Analyse

Les 10 premiers pays les plus équipés d'internet sont, dans l'ordre : 1) Icelande 2) Norvège 3) Suède 4) Danemark 5) Pays-Bas 6) Finlande 7) Suisse 8) Luxembourg 9) Royaume-Unis 10) Allemagne
In [ ]:
df_Europe_et_Asie_centrale_PIB_courant = df_Europe_et_Asie_centrale[df_Europe_et_Asie_centrale['Indicator Name'] == 'GDP, PPP (current international $)']
df_Europe_et_Asie_centrale_PIB_courant.head()
Out[ ]:
Country Name Region Income Group Indicator Name Année value
193 Austria Europe & Central Asia High income: OECD GDP, PPP (current international $) 2000 2.351994e+11
196 Belgium Europe & Central Asia High income: OECD GDP, PPP (current international $) 2000 2.866961e+11
212 Switzerland Europe & Central Asia High income: OECD GDP, PPP (current international $) 2000 2.566722e+11
223 Cyprus Europe & Central Asia High income: nonOECD GDP, PPP (current international $) 2000 1.505738e+10
224 Czech Republic Europe & Central Asia High income: OECD GDP, PPP (current international $) 2000 1.659100e+11
In [ ]:
sns.catplot(x='Année', y='value', data=df_Europe_et_Asie_centrale_PIB_courant, hue='Country Name', col='Indicator Name', kind='point', height = 10)
Out[ ]:
<seaborn.axisgrid.FacetGrid at 0x12863cca850>

Analyse

Dans l'ordre, les pays comportant le PIB les plus important sont l'Allemagne, la Russie, puis derrière, la France et le Royaume-Unis.

In [ ]:
df_Europe_et_Asie_centrale_PIB_constant_2011 = df_Europe_et_Asie_centrale[df_Europe_et_Asie_centrale['Indicator Name'] == 'GDP, PPP (constant 2011 international $)']
df_Europe_et_Asie_centrale_PIB_constant_2011.head()
Out[ ]:
Country Name Region Income Group Indicator Name Année value
375 Austria Europe & Central Asia High income: OECD GDP, PPP (constant 2011 international $) 2000 3.124549e+11
378 Belgium Europe & Central Asia High income: OECD GDP, PPP (constant 2011 international $) 2000 3.812301e+11
396 Switzerland Europe & Central Asia High income: OECD GDP, PPP (constant 2011 international $) 2000 3.647873e+11
407 Cyprus Europe & Central Asia High income: nonOECD GDP, PPP (constant 2011 international $) 2000 2.088022e+10
408 Czech Republic Europe & Central Asia High income: OECD GDP, PPP (constant 2011 international $) 2000 2.173448e+11
In [ ]:
sns.catplot(x='Année', y='value', data=df_Europe_et_Asie_centrale_PIB_constant_2011, hue='Country Name', col='Indicator Name', kind='point', height = 10)
Out[ ]:
<seaborn.axisgrid.FacetGrid at 0x12863f7f9a0>

Analyse

En étudiant le PIB courant, nous constatons que finalement les PIB de l'Allemagne et de la Russie sont identiques, une fois l'effet de l'inflation supprimé. Les deux pays ont donc des PIB similaires.

In [ ]:
df_Europe_et_Asie_centrale_PÏB_par_habitant_courant = df_Europe_et_Asie_centrale[df_Europe_et_Asie_centrale['Indicator Name'] == 'GDP per capita, PPP (current international $)']
df_Europe_et_Asie_centrale_PÏB_par_habitant_courant.head()
Out[ ]:
Country Name Region Income Group Indicator Name Année value
560 Austria Europe & Central Asia High income: OECD GDP per capita, PPP (current international $) 2000 29357.485725
563 Belgium Europe & Central Asia High income: OECD GDP per capita, PPP (current international $) 2000 27966.938804
579 Switzerland Europe & Central Asia High income: OECD GDP per capita, PPP (current international $) 2000 35727.068352
590 Cyprus Europe & Central Asia High income: nonOECD GDP per capita, PPP (current international $) 2000 21695.794534
591 Czech Republic Europe & Central Asia High income: OECD GDP per capita, PPP (current international $) 2000 16178.353832
In [ ]:
sns.catplot(x='Année', y='value', data=df_Europe_et_Asie_centrale_PÏB_par_habitant_courant, hue='Country Name', col='Indicator Name', kind='point', height = 10)
Out[ ]:
<seaborn.axisgrid.FacetGrid at 0x12860470bb0>

Analyse

Le Luxembourg dégage le PIB par habitant le plus important de tous, toutefois, il s'agit d'un petit pays. En 2ème et 3ème position nous avons l'Irlande, la Suisse, la Norvège. Pour information la majorité des revenus de ces 3 premiers pays proviennent ne provient probablement pas de leur industrie, mais de leur état de paradis fiscaux, se rapportant à une faible population du pays.

La Norvège quant à elle se distingue en tant que 4ème pays.

In [ ]:
df_Europe_et_Asie_centrale_PIB_par_habitant_constant_2011 = df_Europe_et_Asie_centrale[df_Europe_et_Asie_centrale['Indicator Name'] == 'GDP per capita, PPP (constant 2011 international $)']
df_Europe_et_Asie_centrale_PIB_par_habitant_constant_2011.head()
Out[ ]:
Country Name Region Income Group Indicator Name Année value
742 Austria Europe & Central Asia High income: OECD GDP per capita, PPP (constant 2011 internation... 2000 39000.480953
745 Belgium Europe & Central Asia High income: OECD GDP per capita, PPP (constant 2011 internation... 2000 37188.643693
763 Switzerland Europe & Central Asia High income: OECD GDP per capita, PPP (constant 2011 internation... 2000 50775.976339
774 Cyprus Europe & Central Asia High income: nonOECD GDP per capita, PPP (constant 2011 internation... 2000 30085.774122
775 Czech Republic Europe & Central Asia High income: OECD GDP per capita, PPP (constant 2011 internation... 2000 21193.901714
In [ ]:
sns.catplot(x='Année', y='value', data=df_Europe_et_Asie_centrale_PIB_par_habitant_constant_2011, hue='Country Name', col='Indicator Name', kind='point', height = 10)
Out[ ]:
<seaborn.axisgrid.FacetGrid at 0x128652a0b80>

Analyse

En supprimant l'effet de l'inflation, l'ordre de notre classement est modifié : 1er : Le Luxembourg 2ème : La Norvège 3ème : La Croatie 4ème : La Suisse Cet indicateur étant plus fiable, nous le préférerons au précédent.
In [ ]:
df_Europe_et_Asie_centrale_pop_growth = df_Europe_et_Asie_centrale[df_Europe_et_Asie_centrale['Indicator Name'] == 'Population growth (annual %)']
df_Europe_et_Asie_centrale_pop_growth.head()
Out[ ]:
Country Name Region Income Group Indicator Name Année value
927 Austria Europe & Central Asia High income: OECD Population growth (annual %) 2000 0.240467
930 Belgium Europe & Central Asia High income: OECD Population growth (annual %) 2000 0.242518
947 Switzerland Europe & Central Asia High income: OECD Population growth (annual %) 2000 0.561955
959 Cyprus Europe & Central Asia High income: nonOECD Population growth (annual %) 2000 1.844130
960 Czech Republic Europe & Central Asia High income: OECD Population growth (annual %) 2000 -0.280414
In [ ]:
sns.catplot(x='Année', y='value', data=df_Europe_et_Asie_centrale_pop_growth, hue='Country Name', col='Indicator Name', kind='point', height = 10)
Out[ ]:
<seaborn.axisgrid.FacetGrid at 0x12866a8af70>

Analyse

La plus forte croissance de population vient du Luxembourg, de l'Irlande et de Chypre.

In [ ]:
df_Europe_et_Asie_centrale_pop_totale = df_Europe_et_Asie_centrale[df_Europe_et_Asie_centrale['Indicator Name'] == 'Population, total']
df_Europe_et_Asie_centrale_pop_totale.head()
Out[ ]:
Country Name Region Income Group Indicator Name Année value
1116 Austria Europe & Central Asia High income: OECD Population, total 2000 8011566.0
1119 Belgium Europe & Central Asia High income: OECD Population, total 2000 10251250.0
1136 Switzerland Europe & Central Asia High income: OECD Population, total 2000 7184250.0
1148 Cyprus Europe & Central Asia High income: nonOECD Population, total 2000 943286.0
1149 Czech Republic Europe & Central Asia High income: OECD Population, total 2000 10255063.0
In [ ]:
sns.catplot(x='Année', y='value', data=df_Europe_et_Asie_centrale_pop_totale, hue='Country Name', col='Indicator Name', kind='point', height = 10)
Out[ ]:
<seaborn.axisgrid.FacetGrid at 0x12863c90b80>

Analyse

La population la plus importante se situe en Russie, puis en Allemagne. Ces deux pays ne s'étant pas démarqué sur la variable PIB par habitant, mais sur celle de la population, ils pourraient être retenus pour une stratégie de pénétration du marché (notamment en Russie).

Indicateurs relatifs à l'éducation

In [ ]:
df_Europe_et_Asie_centrale_2dary_educ = df_Europe_et_Asie_centrale[df_Europe_et_Asie_centrale['Indicator Name'] == 'Government expenditure on secondary education as % of GDP (%)']
df_Europe_et_Asie_centrale_2dary_educ.head()
Out[ ]:
Country Name Region Income Group Indicator Name Année value
1308 Latvia Europe & Central Asia High income: nonOECD Government expenditure on secondary education ... 2000 2.57449
1313 Slovak Republic Europe & Central Asia High income: OECD Government expenditure on secondary education ... 2000 1.97573
2645 Latvia Europe & Central Asia High income: nonOECD Government expenditure on secondary education ... 2001 2.81808
2650 Slovak Republic Europe & Central Asia High income: OECD Government expenditure on secondary education ... 2001 2.02052
3982 Latvia Europe & Central Asia High income: nonOECD Government expenditure on secondary education ... 2002 2.96802
In [ ]:
sns.catplot(x='Année', y='value', data=df_Europe_et_Asie_centrale_2dary_educ, hue='Country Name', col='Indicator Name', kind='point', height = 10)
Out[ ]:
<seaborn.axisgrid.FacetGrid at 0x1286799a190>
In [ ]:
df_Europe_et_Asie_centrale_3tiary_educ = df_Europe_et_Asie_centrale[df_Europe_et_Asie_centrale['Indicator Name'] == 'Government expenditure on tertiary education as % of GDP (%)']
df_Europe_et_Asie_centrale_3tiary_educ.head()
Out[ ]:
Country Name Region Income Group Indicator Name Année value
1328 Latvia Europe & Central Asia High income: nonOECD Government expenditure on tertiary education a... 2000 0.86024
1332 Slovak Republic Europe & Central Asia High income: OECD Government expenditure on tertiary education a... 2000 0.71140
2665 Latvia Europe & Central Asia High income: nonOECD Government expenditure on tertiary education a... 2001 0.85286
2669 Slovak Republic Europe & Central Asia High income: OECD Government expenditure on tertiary education a... 2001 0.80926
4002 Latvia Europe & Central Asia High income: nonOECD Government expenditure on tertiary education a... 2002 0.86497
In [ ]:
sns.catplot(x='Année', y='value', data=df_Europe_et_Asie_centrale_3tiary_educ, hue='Country Name', col='Indicator Name', kind='point', height = 10)
Out[ ]:
<seaborn.axisgrid.FacetGrid at 0x128682abc10>

Analyse

Les données ne sont pas suffisamment probantes pour établir des conclusions.

Résumé

Description générale En comparant les deux graphiques ci-dessus, nous pouvons remarquer que l'Amérique du nord comporte le PIB le plus important et de loin, de 2000 à 2017. Bien que l'amérique du Nord comporte le PIB le plus important du point de vue globale (graphique 1 et 2) nous remarquons qu'elle ne représente pas la région du monde la plus riche. Le PIB par habitant vise à mettre en avant le niveau de richesse du pays, rapporté au nombre d'habitants. Il constitue donc un indicateur du niveau de vie des habitants, aussi appelé pouvoir d'achat. Toutefois, il ne permet pas de prendre en compte les inégalités de richesses. Ainsi nous remarquons qu'entre 2012 et 2017, l'Afrique centrale et du nord s'est vue rattrapé par la région d'Asie de l'Est/Pacifique à partir de 2012. L'Amérique du Nord est quant à elle 3ème de ce point de vue. En supprimant l'effet de l'inflation, nous constatons que la tendance semble se confirmer. Parmis l'ensemble des pays comportant un haut niveau de revenus, nous remarquons que seule région d'Europe et Asie centrale se démarque. Elle semble répondre à la fois aux critères de niveau de richesse élevés, dans chaque pays concernés et être la seule à investir dans l'éducation secondaire. Toutefois, ceci étant peu probable, il est possible que les données n'aient pas été renseignées pour les autres régions du monde. ------------------------------- Par région Asie de l'Est et pacifique¶ Internet : 1) Korée 2 Japon 3) Nouvelle-Zélande 4) Australie 5) Hong-kong La Korée et le japon sont les plus équipés en internet, suivi par la nouvelle-zélande, l'australie et Hong-kong, le tout approchant les 90%. PIB 1) Japon 2) Korée Le Japon est le pays dégagant le plus de richesse, produisant plus du triple de son suiveur, la Korée. PIB /habitant 1) Macao 2) Singapour 3) Brunei Darussalam Macao, une région administrative de la Chine (elle constitue donc un segment distinct sur le plan statistique) possède le niveau de richesse par habitant le plus élevé (mais a nettement baissé entre 2013 et 2017), suivi par Singapour et Brunei Darussalam. Ce dernier est un micro-état et il est peu probable qu'il constitue un pays d'implantation intéressant. Population : 1) Japon 2) Korée Note : La Chine est absente des données Le Japon semble ici comporter la population la plus importante de la région Asie de l'Est et Pacifique, toutefois cela semble très peu probable; du fait de l'absence d'autres pays majeur (comme la Chine) dans la représentation graphique. Sans la présence de ces données il semble difficile de prendre du recul sur le niveau élevé de richesse par habitant de Macao (Chine). Si nous la retenons pas dnans ce classement, le Japon et la Korée sont les meilleurs cibles. Education secondaire et supérieure : pas de données Nord Afrique et Centre Est Internet 1) Bahrain 2) Emirats Arabes Unis 3) Qatar On constate que 3 pays se distingue nettement : Bahrain , les Emirats Arabes Unis et le Qatar sont équipés à + de 95% d'internet en 2017. PIB 1) Arabie Saoudite 2) Emirats Arabes Unis Le PIB de l'arabie saoudite dépasse de loin celui des autres pays, le deuxième étant les émirats arabes unis. PIB /habitant 1) Qatar 2) Koweit 3) Emirats Arabes Unis Le Qatar est le pays le plus riche par habitant, suivi de loin par le koweit et les émirats arabes unis. Croissance de la population 1) Qatar 2) Emirats Arbes Unis Le qatar puis les émirats arabes unis connaissent la plus forte croissance de leur population. Population (totale) 1) Arabie Saoudite 2) Emirats arabes 3) Israël L'arabie saoudite possède de très loin la population la plus importante devant les émirats arabes et israël. Education secondaire et supérieure : pas de données Amérique du Nord Internet 1) Canada 2) Etats-Unis Le canada est dôté à plus de 90% d'internet et les Etats unis à plus de 70%. PIB 1) Etats - Unis 2) Canada Toutefois le canada ne dégage que très peu de richesse de sa production contrairement aux Etats-Unis. PIB/habitant 1) Etats-Unis 2) Canada Le niveau de richesse par habitant des Etats-Unis est bien plus important qu'au Canada, le rendant plus attractif. Les Etats-Unis comporte de très loin la population la plus importante, avec un PIB par habitant extrêmement élevé. Les perspective de profit y sont importantes. Europe et Asie centrale¶ Les 10 premiers pays les plus équipés d'internet sont, dans l'ordre : 1) Icelande 2) Norvège 3) Suède 4) Danemark 5) Pays-Bas 6) Finlande 7) Suisse 8) Luxembourg 9) Royaume-Unis 10) Allemagne Dans l'ordre, les pays comportant le PIB les plus important sont l'Allemagne, la Russie, puis derrière, la France et le Royaume-Unis. En étudiant le PIB courant, nous constatons que finalement les PIB de l'Allemagne et de la Russie sont identiques, une fois l'effet de l'inflation supprimé. Les deux pays ont donc des PIB similaires. Le Luxembourg dégage le PIB le plus important de tous, toutefois, il s'agit d'un petit pays. En 2ème et 3ème position nous avons l'Irlande, la Suisse, la Norvège. Pour information la majorité des revenus de ces 3 premiers pays proviennent ne provient probablement pas de leur industrie, mais de leur état de paradis fiscaux, se rapportant à une faible population du pays. La Norvège quant à elle se distingue en tant que 4ème pays. En supprimant l'effet de l'inflation, l'ordre de notre classement est modifié : 1er : Le Luxembourg 2ème : La Norvège 3ème : La Croatie 4ème : La Suisse Cet indicateur étant plus fiable, nous le préférerons au précédent. La plus forte croissance de population vient du Luxembourg, de l'Irlande et de Chypre. La population la plus importante se situe en Russie, puis en Allemagne. Ces deux pays ne s'étant pas démarqué sur la variable PIB par habitant, mais sur celle de la population, ils pourraient être retenus pour une stratégie de pénétration du marché (notamment en Russie). Données d'éducation secondaire et supérieure : Les données ne sont pas suffisamment probantes pour établir des conclusions. moy_par_indicateur_East_Asia_Pacific moy_par_indicateur_Middle_East_and_North_Africa moy_par_indicateur_North_America moy_par_indicateur_Europe_et_Asie_centrale
In [ ]:
var_to_study
Out[ ]:
('GDP, PPP (current international $)',
 'GDP, PPP (constant 2011 international $)',
 'GDP per capita, PPP (current international $)',
 'GDP per capita, PPP (constant 2011 international $)',
 'Government expenditure on secondary education as % of GDP (%)',
 'Government expenditure on tertiary education as % of GDP (%)',
 'Internet users (per 100 people)',
 'Population growth (annual %)',
 'Population, total')

Asie de l'Est et Pacifique

Moyenne d'utilisation d'internet

In [ ]:
East_Asia_and_pacific_internet = moy_par_indicateur_East_Asia_Pacific[moy_par_indicateur_East_Asia_Pacific['Indicator Name'] == 'Internet users (per 100 people)']
East_Asia_and_pacific_internet.head(5)
Out[ ]:
Indicator Name Country Name value
36 Internet users (per 100 people) Korea, Rep. 76.360066
32 Internet users (per 100 people) Australia 72.753982
38 Internet users (per 100 people) New Zealand 72.001721
35 Internet users (per 100 people) Japan 69.788079
39 Internet users (per 100 people) Singapore 64.844272

Moyenne du PIB / habitant constant

In [ ]:
East_Asia_and_pacific_PIB_par_habitant_constant = moy_par_indicateur_East_Asia_Pacific[moy_par_indicateur_East_Asia_Pacific['Indicator Name'] == 'GDP per capita, PPP (constant 2011 international $)']
East_Asia_and_pacific_PIB_par_habitant_constant.head(5)
Out[ ]:
Indicator Name Country Name value
5 GDP per capita, PPP (constant 2011 internation... Macao SAR, China 83435.821972
1 GDP per capita, PPP (constant 2011 internation... Brunei Darussalam 81089.913841
7 GDP per capita, PPP (constant 2011 internation... Singapore 66847.734764
2 GDP per capita, PPP (constant 2011 internation... Hong Kong SAR, China 44658.061736
0 GDP per capita, PPP (constant 2011 internation... Australia 40241.996686

Moyenne du total de la population, par pays, sur les dernières année

In [ ]:
East_Asia_and_pacific_population = moy_par_indicateur_East_Asia_Pacific[moy_par_indicateur_East_Asia_Pacific['Indicator Name'] == 'Population, total']
East_Asia_and_pacific_population.head(5)
Out[ ]:
Indicator Name Country Name value
51 Population, total Japan 1.275907e+08
52 Population, total Korea, Rep. 4.910551e+07
48 Population, total Australia 2.145270e+07
50 Population, total Hong Kong SAR, China 6.969818e+06
55 Population, total Singapore 4.781768e+06

Précision statistique

Après recherche la population de Macao en 2017 ne dépasse pas les 625 000 habitants.

Source : https://data.worldbank.org/indicator/SP.POP.TOTL?locations=MO&name_desc=false

Moyenne du PIB constant

In [ ]:
East_Asia_and_pacific_GDP_constant = moy_par_indicateur_East_Asia_Pacific[moy_par_indicateur_East_Asia_Pacific['Indicator Name'] == 'GDP, PPP (constant 2011 international $)']
East_Asia_and_pacific_GDP_constant.head(5)
Out[ ]:
Indicator Name Country Name value
19 GDP, PPP (constant 2011 international $) Japan 4.567899e+12
20 GDP, PPP (constant 2011 international $) Korea, Rep. 1.390367e+12
16 GDP, PPP (constant 2011 international $) Australia 8.675901e+11
23 GDP, PPP (constant 2011 international $) Singapore 3.252734e+11
18 GDP, PPP (constant 2011 international $) Hong Kong SAR, China 3.126940e+11

Analyse

De manière générale, Singapour est le pays cible à retenir. Le pays réponds très favorablement besoin d'équipement en internet, comporte une population riche et dense.

Dans le cadre d'une offre à destination des états ou d'une stratégie de pénétration du marché(offre plus abordable), le Japon, la Korée et l'Australie sont de très bonnes cibles.

Centre Est et Afrique du Nord

In [ ]:
Centre_Est_et_Afrique_du_Nord_internet = moy_par_indicateur_Middle_East_and_North_Africa[moy_par_indicateur_Middle_East_and_North_Africa['Indicator Name'] == 'Internet users (per 100 people)']
Centre_Est_et_Afrique_du_Nord_internet.head(5)
Out[ ]:
Indicator Name Country Name value
37 Internet users (per 100 people) United Arab Emirates 59.313105
30 Internet users (per 100 people) Bahrain 50.685048
33 Internet users (per 100 people) Malta 50.496379
31 Internet users (per 100 people) Israel 48.921582
35 Internet users (per 100 people) Qatar 48.269271
In [ ]:
Centre_Est_et_Afrique_du_Nord_PIB_par_habitant_constant = moy_par_indicateur_Middle_East_and_North_Africa[moy_par_indicateur_Middle_East_and_North_Africa['Indicator Name'] == 'GDP per capita, PPP (constant 2011 international $)']
Centre_Est_et_Afrique_du_Nord_PIB_par_habitant_constant.head(5)
Out[ ]:
Indicator Name Country Name value
5 GDP per capita, PPP (constant 2011 internation... Qatar 117430.649881
2 GDP per capita, PPP (constant 2011 internation... Kuwait 79443.822968
7 GDP per capita, PPP (constant 2011 internation... United Arab Emirates 77914.987870
6 GDP per capita, PPP (constant 2011 internation... Saudi Arabia 46006.635291
4 GDP per capita, PPP (constant 2011 internation... Oman 42966.980635
In [ ]:
Centre_Est_et_Afrique_du_Nord_population = moy_par_indicateur_Middle_East_and_North_Africa[moy_par_indicateur_Middle_East_and_North_Africa['Indicator Name'] == 'Population, total']
Centre_Est_et_Afrique_du_Nord_population.head(5)
Out[ ]:
Indicator Name Country Name value
52 Population, total Saudi Arabia 2.620014e+07
47 Population, total Israel 7.368076e+06
53 Population, total United Arab Emirates 6.505238e+06
50 Population, total Oman 3.009788e+06
48 Population, total Kuwait 2.839027e+06
In [ ]:
Centre_Est_et_Afrique_du_Nord_GDP_constant = moy_par_indicateur_Middle_East_and_North_Africa[moy_par_indicateur_Middle_East_and_North_Africa['Indicator Name'] == 'GDP, PPP (constant 2011 international $)']
Centre_Est_et_Afrique_du_Nord_GDP_constant.head(5)
Out[ ]:
Indicator Name Country Name value
21 GDP, PPP (constant 2011 international $) Saudi Arabia 1.216317e+12
22 GDP, PPP (constant 2011 international $) United Arab Emirates 4.694754e+11
17 GDP, PPP (constant 2011 international $) Kuwait 2.227807e+11
16 GDP, PPP (constant 2011 international $) Israel 2.138769e+11
20 GDP, PPP (constant 2011 international $) Qatar 1.751089e+11

Analyse

D'une manière générale, les Emirats Arabes Unis représentent le meilleur candidat. Dans le cadre d'une offre à destination des états ou d'une stratégie de pénétration du marché(offre plus abordable), les Emirats Arabes Unis, l'Arabie Saoudite, Israël et le Koweit sont de très bonnes cibles.

Amérique du Nord

In [ ]:
Amerique_du_Nord_internet = moy_par_indicateur_North_America[moy_par_indicateur_North_America['Indicator Name'] == 'Internet users (per 100 people)']
Amerique_du_Nord_internet.head(5)
Out[ ]:
Indicator Name Country Name value
8 Internet users (per 100 people) Canada 75.002310
9 Internet users (per 100 people) United States 67.385319
In [ ]:
Amerique_du_Nord_PIB_par_habitant_constant = moy_par_indicateur_North_America[moy_par_indicateur_North_America['Indicator Name'] == 'GDP per capita, PPP (constant 2011 international $)']
Amerique_du_Nord_PIB_par_habitant_constant.head(5)
Out[ ]:
Indicator Name Country Name value
1 GDP per capita, PPP (constant 2011 internation... United States 49605.441304
0 GDP per capita, PPP (constant 2011 internation... Canada 40724.934354
In [ ]:
Amerique_du_Nord_population = moy_par_indicateur_North_America[moy_par_indicateur_North_America['Indicator Name'] == 'Population, total']
Amerique_du_Nord_population.head(5)
Out[ ]:
Indicator Name Country Name value
13 Population, total United States 3.033803e+08
12 Population, total Canada 3.338018e+07
In [ ]:
Amerique_du_Nord_GDP_constant = moy_par_indicateur_North_America[moy_par_indicateur_North_America['Indicator Name'] == 'GDP, PPP (constant 2011 international $)']
Amerique_du_Nord_GDP_constant.head(5)
Out[ ]:
Indicator Name Country Name value
5 GDP, PPP (constant 2011 international $) United States 1.507389e+13
4 GDP, PPP (constant 2011 international $) Canada 1.362130e+12

Analyse

D'une manière générale, les Etats-Unis représentent le meilleur candidat. Dans le cadre d'une offre à destination des individus le pays reste un bon choix, le Canada reste une cible potentielle.

Europe et Asie centrale

In [ ]:
Europe_Asie_internet = moy_par_indicateur_Europe_et_Asie_centrale[moy_par_indicateur_Europe_et_Asie_centrale['Indicator Name'] == 'Internet users (per 100 people)']
Europe_Asie_internet.head(10)
Out[ ]:
Indicator Name Country Name value
127 Internet users (per 100 people) Iceland 86.275284
134 Internet users (per 100 people) Norway 85.045404
141 Internet users (per 100 people) Sweden 83.059642
121 Internet users (per 100 people) Denmark 81.444586
133 Internet users (per 100 people) Netherlands 79.873268
123 Internet users (per 100 people) Finland 76.654376
142 Internet users (per 100 people) Switzerland 75.583391
132 Internet users (per 100 people) Luxembourg 74.480298
143 Internet users (per 100 people) United Kingdom 73.480179
125 Internet users (per 100 people) Germany 70.444952
In [ ]:
Europe_Asie_PIB_par_habitant_constant = moy_par_indicateur_Europe_et_Asie_centrale[moy_par_indicateur_Europe_et_Asie_centrale['Indicator Name'] == 'GDP per capita, PPP (constant 2011 international $)']
Europe_Asie_PIB_par_habitant_constant.head(5)
Out[ ]:
Indicator Name Country Name value
16 GDP per capita, PPP (constant 2011 internation... Luxembourg 90040.598602
18 GDP per capita, PPP (constant 2011 internation... Norway 62290.965809
26 GDP per capita, PPP (constant 2011 internation... Switzerland 54497.319760
12 GDP per capita, PPP (constant 2011 internation... Ireland 46818.390577
17 GDP per capita, PPP (constant 2011 internation... Netherlands 44685.981508
In [ ]:
Europe_Asie_population = moy_par_indicateur_Europe_et_Asie_centrale[moy_par_indicateur_Europe_et_Asie_centrale['Indicator Name'] == 'Population, total']
Europe_Asie_population.head(10)
Out[ ]:
Indicator Name Country Name value
193 Population, total Russian Federation 1.438984e+08
181 Population, total Germany 8.186383e+07
180 Population, total France 6.416499e+07
199 Population, total United Kingdom 6.193559e+07
185 Population, total Italy 5.876462e+07
196 Population, total Spain 4.468464e+07
191 Population, total Poland 3.811671e+07
189 Population, total Netherlands 1.649074e+07
182 Population, total Greece 1.096416e+07
173 Population, total Belgium 1.075955e+07
In [ ]:
Europe_Asie_GDP_constant = moy_par_indicateur_Europe_et_Asie_centrale[moy_par_indicateur_Europe_et_Asie_centrale['Indicator Name'] == 'GDP, PPP (constant 2011 international $)']
Europe_Asie_GDP_constant.head(10)
Out[ ]:
Indicator Name Country Name value
65 GDP, PPP (constant 2011 international $) Germany 3.289937e+12
77 GDP, PPP (constant 2011 international $) Russian Federation 3.062156e+12
64 GDP, PPP (constant 2011 international $) France 2.361268e+12
83 GDP, PPP (constant 2011 international $) United Kingdom 2.278731e+12
69 GDP, PPP (constant 2011 international $) Italy 2.136631e+12
80 GDP, PPP (constant 2011 international $) Spain 1.439628e+12
75 GDP, PPP (constant 2011 international $) Poland 7.636609e+11
73 GDP, PPP (constant 2011 international $) Netherlands 7.373716e+11
57 GDP, PPP (constant 2011 international $) Belgium 4.327741e+11
82 GDP, PPP (constant 2011 international $) Switzerland 4.206516e+11
In [ ]:
Europe_Asie_educ_2dary_as_purcent_GDP = moy_par_indicateur_Europe_et_Asie_centrale[moy_par_indicateur_Europe_et_Asie_centrale['Indicator Name'] == 'Government expenditure on secondary education as % of GDP (%)']
Europe_Asie_educ_2dary_as_purcent_GDP.head(5)
Out[ ]:
Indicator Name Country Name value
112 Government expenditure on secondary education ... Latvia 2.238174
113 Government expenditure on secondary education ... Slovak Republic 1.885144
In [ ]:
Europe_Asie_educ_3rd_as_purcent_GDP = moy_par_indicateur_Europe_et_Asie_centrale[moy_par_indicateur_Europe_et_Asie_centrale['Indicator Name'] == 'Government expenditure on tertiary education as % of GDP (%)']
Europe_Asie_educ_3rd_as_purcent_GDP.head(5)
Out[ ]:
Indicator Name Country Name value
114 Government expenditure on tertiary education a... Latvia 0.854245
115 Government expenditure on tertiary education a... Slovak Republic 0.844549

Analyse

Dans le cadre d'une offre "premium", la Norvège et les Pays-Bas sont de très bons candidats. Dans le cadre d'une offre à destination des états ou d'une stratégie de pénétration du marché (offre plus abordable), l'Allemagne, l'Angleterre reste de d'excellente cibles potentielle. Toutefois, la Russie serait le meilleur candidat, sous réserve d'un équipement suffisant en internet. La Russie comprote un fort PIB, une forte population mais le taux d'équiepement étant une donnée cruciale absente, nous ne pouvons pas la retenir.

In [ ]: